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<title>Cloud.Sitez &#45; : GOOGL</title>
<link>http://cloud.sitez.gr/rss/category/googl</link>
<description>Cloud.Sitez &#45; : GOOGL</description>
<dc:language>en</dc:language>
<dc:rights>Sitez.Gr &#45; Copyright © 2021 &#45; All Rights Reserved.</dc:rights>

<item>
<title>Arize, Vertex AI API: Evaluation workflows to accelerate generative app development and AI ROI</title>
<link>http://cloud.sitez.gr/arize-vertex-ai-api-evaluation-workflows-to-accelerate-generative-app-development-and-ai-roi</link>
<guid>http://cloud.sitez.gr/arize-vertex-ai-api-evaluation-workflows-to-accelerate-generative-app-development-and-ai-roi</guid>
<description><![CDATA[ In the rapidly evolving landscape of artificial intelligence, enterprise AI engineering teams must constantly seek cutting-edge solutions to drive innovation, enhance productivity, and maintain a competitive edge. In leveraging an AI observability and evaluation platform like Arize AI with the advanced capabilities of Google’s suite of AI tools, enterprises looking to push the boundaries of what’s possible with their AI applications have a robust, compelling option.
As a state-of-the-art large language model (LLM) with multi-modal capabilities, Vertex AI API serving Gemini 1.5 Pro offers enterprise teams a powerful model that can be integrated across a wide range of applications and use cases. From improving customer interactions and automating complex processes to enhancing data analysis and decision-making, the potential to transform business operations is significant.
By adopting Vertex AI API for Gemini, enterprise AI teams can:


Accelerate development: Leverage advanced natural language processing and generation capabilities to streamline code development, debugging, and documentation processes.


Enhance customer experiences: Implement sophisticated chatbots and virtual assistants capable of understanding and responding to customer queries across multiple modalities.


Boost data analysis: Utilize the ability to process and interpret various data types, including text, images, and audio, for more comprehensive and insightful data analysis.


Improve decision-making: Harness advanced reasoning capabilities to provide data-driven insights and recommendations to support strategic decision-making.


Drive innovation: Explore new possibilities in product development, research, and creative processes by tapping into Vertex AI’s generative capabilities.


Teams using the Vertex AI API further gain from implementing a telemetry system, or AI observability and LLM evaluation, as they’re developing generative applications to validate performance and accelerate the iteration cycle. By adopting Arize AI in tandem with their Google AI tools, AI teams can:


Help ensure application reliability: Continuously validate and monitor generative app performance as input data shifts and new use cases arise, to quickly address issues in development and after deployment.


Speed development cycles: Rapidly iterate using pre-production app evaluations and workflows to test and compare the results of various prompt iterations.


Implement guardrails for protection: Systematically test app responses to a wide range of inputs and edge cases to ensure outputs comply in the boundaries of expectations.


Make improvements with dynamic data: Automatically flag low-performing sessions for review and identify challenging or ambiguous examples for further analysis and fine-tuning. 


Consistent evaluation from development to deployment: Use Arize’s open-source evaluation solution during development, alongside an enterprise-ready platform as applications become ready for production.



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Solutions to common challenges afflicting AI engineering teams
In working with hundreds of AI engineering teams building and deploying generative-powered applications, a common set of challenges emerged: 


Small changes can lead to performance regressions – even minor alterations in prompts or underlying data can result in expected degradations. It’s difficult to anticipate or track down these regressions.


Hard to discover data for testing and improvement – identifying edge cases, underrepresented scenarios or high-impact failure modes requires complex data mining techniques to extract meaningful subsets of data. 


Bad LLM responses can lead to outsized business impact – a single factually incorrect or inappropriate response can result in legal issues, loss of trust, or financial liabilities. 


Arize’s AI observability and evaluation platform enables engineering teams to tackle these challenges head on, building a foundation during the app development phase to carry through to online production observability. Let’s delve deeper into the specific applications and integration strategies for Arize and Vertex AI, and how an enterprise AI engineering team can build better AI using the two solutions together. 







  
    
      
  

    

      
      
        
        
        
        
      
    

  
      
    
  





Gain visibility with LLM tracing in development
Arize’s LLM tracing capabilities provides visibility into each call in an LLM-powered system to facilitate application development and troubleshooting. This is especially critical for systems that implement an orchestration or agentic framework, as those abstractions can mask an immense number of distributed system calls that are nearly impossible to debug without programmatic tracing. 
With LLM tracing, teams gain a comprehensive understanding of how the Vertex AI API serving Gemini 1.5 Pro processes input data through each layer of the application: query, retriev ]]></description>
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<pubDate>Thu, 09 Jan 2025 08:28:38 +0100</pubDate>
<dc:creator>admin</dc:creator>
<media:keywords>Arize, Vertex, API:, Evaluation, workflows, accelerate, generative, app, development, and, ROI</media:keywords>
</item>

<item>
<title>Accelerate retail media success with EPAM and Google Cloud</title>
<link>http://cloud.sitez.gr/accelerate-retail-media-success-with-epam-and-google-cloud</link>
<guid>http://cloud.sitez.gr/accelerate-retail-media-success-with-epam-and-google-cloud</guid>
<description><![CDATA[ Retail media networks, a type of advertising platform that allows retailers to sell ad space on their digital channels to third-party brands, are nothing new. But they are about to change dramatically in the coming year. Consumers are increasingly privacy-conscious and desire more personalized ad recommendations.
EPAM and Google Cloud have been working on retail media solutions for a long time, providing you with data and insights to give you more comprehensive and refined views of your audiences, better measurements, and improved buyer experiences. Together, we have been active participants in the development of this promising “third wave” of digital media, and our experience tells us that the coming year is going to be significant. 
Companies that leverage first-party data most efficiently and use AI and gen AI will see a ROI in retail-media. That’s why we are excited to bring EPAM’s Retail Media Orchestration Toolkit to market now, helping retailers of most sizes capitalize on the promise of the coming year, no matter how mature their retail media operations. With the Retail Media Orchestration Toolkit, you gain customized, in-house retail media operations supported by Google and Google Cloud’s industry-leading digital advertising capabilities, and orchestrated with EPAM’s deep retail expertise. 
With Google Cloud’s AI and gen AI tools and expertise, you can supercharge your first-party data with a level of insight that was previously out of reach — and gain a significant advantage over your competitors.

    aside_block
    

Retail media profits remain elusive
While many retailers are aware of the revenue potential of first-party data and have implemented retail media operations, they still struggle to mature these initiatives. Common roadblocks to maximizing retail media profits include:


Infrastructure unsuited to fulfill the increasing demand for data-driven insights: There are hundreds of retail media networks, so advertisers have an abundance of choice in selecting a network for hosting a campaign. Brands are eager to invest advertising spend in networks that provide detail-rich, data-driven insights, however, many retailers struggle to provide the depth of insights that advertisers seek to justify their retail media advertising spend. Retailers are first and foremost sellers of products — few are retail media experts. And the scale involved for many retailers magnifies the difficulty. 




Inability to provide fast, accurate measurements: Providing closed-loop measurement of campaign performance — and particularly omnichannel measurement across disparate physical and digital customer interactions — requires a level of retail media technology, expertise and orchestration that few retailers possess. 




Lack of technology and resources to provide data clean rooms: Customer data drives retail media. The more comprehensive and in-depth the data, the greater the advertising success. Given that detailed customer data is also often sensitive information, it’s important to protect that data to adhere to industry ethics, maintain customer goodwill and, quite often, adhere to regulatory requirements. Data clean rooms provide a safe environment to use and share customer data among multiple legitimate participants. But the technological challenges involved in maintaining a data clean room are significant, and many retailers have neither the resources nor the expertise to do so.




Difficulties in standardizing workflows and data: Most retail media networks are made up of a patchwork of multiple independent software vendors (ISVs). They  utilize their own processes, procedures and reporting formats. The result is a never-ending stream of incoming reporting data that must be translated to match in-house formats before it can be relayed to advertisers. Many retailers attempt to handle this by manually managing incoming data, resulting in increased staff, degraded performance when reporting to customers, and deflated retail media profits. 


The Retail Media Orchestration Toolkit
Today, retailers can implement in-house, custom solutions for retail media, just as mega-retailers like Albertsons, Kroger, Tesco and Walmart have done.
The Retail Media Orchestration Toolkit was developed under Google Cloud’s Industry Value Network (IVN) initiative in partnership with EPAM and Google Cloud, and leveraging ISV solutions such as Moloco. The Toolkit lets retailers leverage their data to support their retail media operations and serve their advertising clients. 
EPAM has in-depth knowledge of retail media operations, earned through years of experience in developing custom, Google-Cloud-powered, in-house solutions for some of the world’s largest retailers. Google Cloud makes it possible to design and implement custom retail media solutions, offering a holistic, end-to-end platform and solutions for audience capabilities, measurement, media execution and innovation. Based on an innovative cloud-based data foundation known ]]></description>
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<pubDate>Thu, 09 Jan 2025 08:28:35 +0100</pubDate>
<dc:creator>admin</dc:creator>
<media:keywords>Accelerate, retail, media, success, with, EPAM, and, Google, Cloud</media:keywords>
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<item>
<title>Unlocking the power of Spanner: 10 partners to revolutionize your data</title>
<link>http://cloud.sitez.gr/unlocking-the-power-of-spanner-10-partners-to-revolutionize-your-data</link>
<guid>http://cloud.sitez.gr/unlocking-the-power-of-spanner-10-partners-to-revolutionize-your-data</guid>
<description><![CDATA[ In today&#039;s fast-paced digital landscape, businesses need a database solution that can keep up with their evolving demands. Spanner is the always-on, globally consistent, and virtually unlimited-scale database, powering a wide array of modern applications across different industries. Spanner powers a number of Google products with billions of users, including Gmail, and has become the foundation for many other innovative organizations in their data-driven transformations. 
Spanner not only offers predictable single-digit millisecond latencies, virtually unlimited scalability, and a five 9s of availability SLA, but also strong consistency, familiar PostgreSQL-compatible syntax, and ACID transactions. This unique combination makes Spanner an excellent choice not just for relational data but also for read-heavy, key-value workloads. Furthermore, with Spanner granular instances you can start small and grow according to your business needs.
Spanner is constantly evolving to meet the needs of modern businesses. Some of the latest Spanner advancements include enhanced multi-model capabilities, including graph, full-text search, vector search, improved performance for analytical queries with Spanner Data Boost, and the addition of unique enterprise features, such as geo-partitioning and dual-region configurations. These new capabilities, along with compelling price-performance improvements, empower you to further optimize your database operations, gain deeper insights from your data, and build even more resilient and scalable applications.
To help our customers take full advantage of Spanner, we are ensuring it is also backed by a robust ecosystem of technology partners and system integrators ready to help you modernize your applications and migrate to the cloud. 

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Learn more about our Spanner partners
Our Spanner partners offer a wide range of solutions and services, including data integration, analytics, governance, and migration assessment to ensure a smooth transition and optimal use of Spanner. Google Cloud collaborates closely with these partners to guarantee the best possible experience and support as you embark on your cloud transformation journey.

66degrees offers assistance to customers across their full journey including migrations, application code updates, and infrastructure deployment. 
“As a trusted partner in Spanner implementations, 66degrees brings a proven track record of successful database migrations and deep expertise in leveraging Spanner&#039;s capabilities. We have achieved exceptional results in Spanner migrations, including Storj, which we migrated from CockroachDB to Spanner. Our accelerators, such as Route66 and custom data flow templates, streamline the migration process and ensure efficient data transfer. We are committed to partnering with Google to empower organizations to harness the full potential of Spanner, enabling them to achieve scalability, performance, and reliability for their mission-critical applications.” - Daniel Zagales, VP of Data Platforms, 66degrees
Learn more about how 66degrees can help with your database migration journey to Spanner here.

Accenture’s 72,000 database professionals worldwide leverage deep industry and cloud expertise to empower clients to drive innovation and digital transformation.
“Google Cloud Spanner offers great performance with availability, scalability, and performance. Our benchmark result indicated Spanner provided predictable performance while maintaining consistent throughput with low cost. Partnering with Google Cloud on Spanner opportunities allows us to combine our deep industry expertise with cutting-edge database technology on Google Cloud, empowering our clients to unlock new levels of scalability, resilience, and real-time insights that drive their digital transformation journeys to the cloud.&quot; - Weidong Zhou, Principal Director, Accenture.
Learn more about how Accenture can help you unleash the full potential of human-centric design and Google’s innovative tech.

Amarello has been expanding their capabilities to migrate new customers to Spanner, enabled by a great team of application modernization and database migration experts working together and staying updated on the latest Google Cloud technology.
“Amarello is dedicated to reimagining core business solutions that demand elasticity, high availability, and high transactional processing for Google Cloud customers. Spanner has been proven to be our preferred core technology for delivering mission-critical solutions in any industry.” - Damián Contreras, Managing Partner, Amarello 
Learn more about how Amarello can help you unleash the full potential of Spanner.

Ollion worked alongside Google Cloud to develop the critical migration tooling that helps customers move from Cassandra and DynamoDB to Spanner, building deep experience in the nuances a ]]></description>
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<pubDate>Thu, 09 Jan 2025 08:28:31 +0100</pubDate>
<dc:creator>admin</dc:creator>
<media:keywords>Unlocking, the, power, Spanner:, partners, revolutionize, your, data</media:keywords>
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<item>
<title>A practical guide to synthetic data generation with Gretel and BigQuery DataFrames</title>
<link>http://cloud.sitez.gr/a-practical-guide-to-synthetic-data-generation-with-gretel-and-bigquery-dataframes</link>
<guid>http://cloud.sitez.gr/a-practical-guide-to-synthetic-data-generation-with-gretel-and-bigquery-dataframes</guid>
<description><![CDATA[ In our previous post, we explored how integrating Gretel with BigQuery DataFrames streamlines synthetic data generation while preserving data privacy. To recap, BigQuery DataFrames is a Python client for BigQuery, providing pandas-compatible APIs with computations pushed down to BigQuery. Gretel offers a comprehensive toolbox for synthetic data generation using cutting-edge machine learning techniques, including large language models (LLMs). This integration enables an integrated workflow, allowing users to easily transfer data from BigQuery to Gretel and save the generated results back to BigQuery. 
In this guide, we dive into the technical aspects of generating synthetic data to drive AI/ML innovation, while helping to ensure high-data quality, privacy protection, and compliance with privacy regulations. We begin by working with a BigQuery patient records table, de-identifying the data in Part 1, and then generating synthetic data to save back to BigQuery in Part 2.

    aside_block
    

Setting the stage: Installation and configuration
You can start by using BigQuery Studio as the notebook runtime, with BigFrames pre-installed. We assume you have a Google Cloud project set up and you are familiar with Pandas.
Step 1: Install the Gretel Python client and BigQuery DataFrames:

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Step 2: Initialize the Gretel SDK and BigFrames: You&#039;ll need a Gretel API key to access their services. You can obtain one from the Gretel console. 

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Part 1: De-identifying and processing data with Gretel Transform v2
Before generating synthetic data, de-identifying personally identifiable information (PII) is a crucial first step towards data anonymization. Gretel&#039;s Transform v2 (Tv2) provides a powerful and scalable framework for this and various other data processing tasks. Tv2 combines advanced transformation techniques with named entity recognition (NER) capabilities, enabling efficient handling of large datasets. Beyond PII de-identification, Tv2 can be used for data cleansing, formatting, and other preprocessing steps, making it a versatile tool in the data preparation pipeline. Learn more about Gretel Transform v2.
Step 1: Create a BigFrames DataFrame from your BigQuery table:

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The table below is a subset of the DataFrame we will transform. We hash the `patient_id` column and create replacement first and last names based on the value of the `sex` column.

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Step 2: Transform the data with Gretel:

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Step 3: Explore the de-identified data:

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Below is a comparison of the original vs de-identified data.
Original:

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De-identified:

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Part 2: Generating synthetic data with Navigator Fine Tuning (LLM-based)
Gretel Navigator Fine Tuning (NavFT) generates high-quality, domain-specific synthetic data by fine-tuning pre-trained models on your datasets. Key features include:


Handles multiple data modalities: numeric, categorical, free text, time series, and JSON


Maintains complex relationships across data types and rows


Can introduce meaningful new patterns, potentially improving ML/AI task performance


Balances data utility with privacy protection


NavFT builds on Gretel Navigator&#039;s capabilities, enabling the creation of synthetic data that captures the nuances of your specific data, including the distributions and correlations for numeric, categorical, and other column types, while leveraging the strengths of domain-specific pre-trained models. Learn more about Navigator Fine Tuning.
In this example, we will fine-tune a Gretel model on the de-identified data from Part 1.
Step 1: Fine-tune a model:

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Step 2: Fetch the Gretel Synthetic Data Quality Report:

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The image below shows the high-level metrics from the Gretel Synthetic Data Quality Report. Please see the Gretel documentation for more details about how to interpret this report.







  
    
      
  

    

      
      
        
        
        
        
      
    

  
      
    
  





Step 3: Generate synthetic data from the fine-tuned model, evaluate data quality and privacy, and write back to a BQ table.

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Below is a sample of the final synthetic data:

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A few things to note about the synthetic data:


The various modalities (JSON structures, free text) are preserved and fully synthetic while being semantically correct.


Because of the group-by/order-by hyperparameters that were used during fine-tuning, the records are clustered on a per patient basis during generation.


How to use BigQuery with Gretel
This technical guide provides a foundation for leveraging Gretel AI and BigQuery DataFrames to generate and utilize synthetic data. By following these examples and exploring the Gretel documentation, you can unlock the power of synthetic data to enhance your data science, analytics, and AI development workflows while ensuri ]]></description>
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<pubDate>Thu, 09 Jan 2025 08:28:27 +0100</pubDate>
<dc:creator>admin</dc:creator>
<media:keywords>practical, guide, synthetic, data, generation, with, Gretel, and, BigQuery, DataFrames</media:keywords>
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<item>
<title>Empower your teams with self&#45;service Kubernetes using GKE fleets and Argo CD</title>
<link>http://cloud.sitez.gr/empower-your-teams-with-self-service-kubernetes-using-gke-fleets-and-argo-cd</link>
<guid>http://cloud.sitez.gr/empower-your-teams-with-self-service-kubernetes-using-gke-fleets-and-argo-cd</guid>
<description><![CDATA[ Managing applications across multiple Kubernetes clusters is complex, especially when those clusters span different environments or even cloud providers. One powerful and secure solution combines Google Kubernetes Engine (GKE) fleets and, Argo CD, a declarative, GitOps continuous delivery tool for Kubernetes. The solution is further enhanced with Connect Gateway and Workload Identity.
This blog post guides you in setting up a robust, team-centric multi-cluster infrastructure with these offerings. We use a sample GKE fleet with application clusters for your workloads and a control cluster to host Argo CD. To streamline authentication and enhance security, we leverage Connect Gateway and Workload Identity, enabling Argo CD to securely manage clusters without the need to manage cumbersome Kubernetes Services Accounts.
On top of this, we incorporate GKE Enterprise Teams to manage access and resources, helping to ensure that each team has the right permissions and namespaces within this secure framework.

    aside_block
    

Finally, we introduce the fleet-argocd-plugin, a custom Argo CD generator designed to simplify cluster management within this sophisticated setup. This plugin automatically imports your GKE Fleet cluster list into Argo CD and maintains synchronized cluster information, making it easier for platform admins to manage resources and for application teams to focus on deployments.
Follow along as we:


Create a GKE fleet with application and control clusters


Deploy Argo CD on the control cluster, configured to use Connect Gateway and Workload Identity


Configure GKE Enterprise Teams for granular access control


Install and leverage the fleet-argocd-plugin to manage your secure, multi-cluster fleet with team awareness


By the end, you&#039;ll have a powerful and automated multi-cluster system using GKE Fleets, Argo CD, Connect Gateway, Workload Identity, and Teams, ready to support your organization&#039;s diverse needs and security requirements. Let&#039;s dive in!
Set up multi-cluster infrastructure with GKE fleet and Argo CD
Setting up a sample GKE fleet is a straightforward process: 







  
    
      
  

    

      
      
        
        
        
        
      
    

  
      
    
  





1. Enable the required APIs in the desired Google Cloud Project. We use this project as the fleet host project.
a. gcloud SDK must be installed, and you must be authenticated via gcloud auth login. 

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2. Create application clusters and register them under your fleet host project.

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3. Set up teams on your fleet. Let’s say you have one frontend team with a webserver namespace. 
a. With fleet teams and fleet Namespace, you can control which team accesses specific namespaces on specific clusters.

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4. Now, set up Argo CD and deploy it to the control cluster. Create a new GKE cluster as your application and enable Workload Identity on it. 

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5. Install the Argo CD CLI to interact with the Argo CD API server. Version 2.8.0 or higher is required. Detailed installation instructions can be found via the CLI installation documentation. 
6. Deploy Argo CD on the control cluster.

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Customize the Argo CD generator
Now you’ve got your GKE fleet up and running, and you’ve installed Argo CD on the control cluster. In Argo CD, application clusters are registered with the control cluster by storing their credentials (like API server address and authentication details) as Kubernetes Secrets within the Argo CD namespace. We&#039;ve got a way to make this whole process a lot easier!
The fleet-argocd-plugin is a customized Argo CD plugin generator that takes the hassle out of cluster management by: 


Automatically importing your GKE fleet cluster list into Argo CD and setting up the cluster secret objects for each application cluster 


Keeping an eye on your fleet&#039;s status on Google Cloud, making sure your Argo CD cluster list is always in sync and up-to-date


Now, let’s see how to build and configure the Argo CD generator. 
7. Install fleet-argocd-plugin on your control cluster. 
a. In this demo, we use Cloud Build to build and deploy the fleet-argocd-plugin.

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8. To make sure the fleet-argocd-plugin works as it should, give it the right permissions for fleet management. 
a. Create an IAM service account in your Argo CD control cluster and grant it the appropriate permissions. The setup follows the official onboarding guide of GKE Workload Identity Federation. 

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b. You also need to allow the Google Compute Engine service account to access images from your artifacts repository. 

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9. Run the fleet plugin on your Argo CD control cluster! 

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Demo time
Let&#039;s do a quick check to make sure the GKE fleet and Argo CD are playing nicely together. You should see that the secrets for your application clusters have been automatically generated.

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   ]]></description>
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<pubDate>Thu, 09 Jan 2025 08:28:23 +0100</pubDate>
<dc:creator>admin</dc:creator>
<media:keywords>Empower, your, teams, with, self-service, Kubernetes, using, GKE, fleets, and, Argo</media:keywords>
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<item>
<title>Honoring our 2024 Google Cloud Partner All&#45;stars</title>
<link>http://cloud.sitez.gr/honoring-our-2024-google-cloud-partner-all-stars</link>
<guid>http://cloud.sitez.gr/honoring-our-2024-google-cloud-partner-all-stars</guid>
<description><![CDATA[ At Google Cloud, we’re fortunate to partner with organizations that employ some of the world’s most talented and innovative professionals. Together, we’re reshaping industries, driving customer success, and pushing the boundaries of what’s possible. Our partners are more than collaborators — they’re the change-makers defining the future of business.
The Google Cloud Partner All-stars program celebrates these remarkable people. Each year, we recognize those who go above and beyond, leading with passion, innovation, and a commitment to excellence. These are the people driving our industry forward, and we’re thrilled to honor them for 2024.

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2024 Spotlight: Artificial Intelligence
For 2024, we’re excited to introduce a new category that highlights the power and potential of Artificial Intelligence (AI). As AI redefines the business and technology landscape, we’re proud to recognize those who are not just using AI, but actively shaping its future.
The Artificial Intelligence category honors those visionary leaders spearheading AI initiatives with bold ideas, experimentation, and ethical stewardship. They’re bringing AI from concept to reality, unlocking new possibilities, and driving meaningful results for their clients. These Partner All-stars are building the future, one breakthrough at a time.
What sets Partner All-stars apart? 
The following attributes define the standout qualities of a Partner All-star:
Artificial Intelligence 


Provides a clear vision for AI’s transformative potential in the business


Champions AI initiatives by securing resources, driving adoption, and promoting collaboration


Leads experimentation with AI, generating innovative solutions and tangible results for clients


Demonstrates a commitment to ethical AI practices, ensuring responsible and fair use


Delivery excellence


Top-ranked individuals on Google Cloud’s Delivery Readiness Portal (DRP)


Demonstrates commitment to technical excellence by passing advanced delivery challenge labs and other advanced technical training


Demonstrates excellent knowledge and adoption of Google Cloud delivery enablement methodologies, assets, and offerings


Exhibits expertise through customer project and deployment experience 


Consistently meets and exceeds sales goals and targets


Aligns on shared goals to deliver amazing end-to-end customer experiences 


Prioritizes long-term customer-relationship building over short-term selling


Marketing


Drives strategic programs and key events that address customer concerns and interests


Works across cross-functional teams to ensure the success of key campaigns and events


Takes a data-driven approach to marketing, investing time and resources in programs that drive the biggest impact


Always exploring areas of opportunity and improvement in order to uplevel future work


Sales


Demonstrates commitment to the customer transformation journey


Solutions engineering


Delivers superior customer experiences by keeping professional skills up to date, earning at least one Google technical certification


Embraces customer challenges head-on, taking responsibility for end-to-end solutioning


Works with purpose, providing deliverables in a timely manner while never compromising quality 


Works effectively across joint product areas, leveraging technology in new and innovative ways to address customer needs


Celebrating excellence in 2024
On behalf of the entire Google Cloud team, I want to extend our heartfelt congratulations to the 2024 Google Cloud Partner All-stars, who we have notified of this distinction. Their dedication, innovation, and leadership continue to inspire us and drive success for our customers.
Stay tuned as we celebrate this year’s Partner All-stars and join the conversation by following #PartnerAllstars across social media.
Learn more about Google Cloud Partners. ]]></description>
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<pubDate>Thu, 09 Jan 2025 08:28:23 +0100</pubDate>
<dc:creator>admin</dc:creator>
<media:keywords>Honoring, our, 2024, Google, Cloud, Partner, All-stars</media:keywords>
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<item>
<title>Generative AI with enterprise controls for business users in 24 Hours</title>
<link>http://cloud.sitez.gr/generative-ai-with-enterprise-controls-for-business-users-in-24-hours</link>
<guid>http://cloud.sitez.gr/generative-ai-with-enterprise-controls-for-business-users-in-24-hours</guid>
<description><![CDATA[ Aible is a leader in generating business impact from AI in less than 30 days, helping teams use AI to extract enterprise value from raw enterprise data with solutions for customer acquisition, churn prevention, demand prediction, preventative maintenance, and more. After previously leveraging BigQuery’s serverless architecture to reduce analytics costs, Aible is now collaborating with Google Cloud to enable customers to build, train, and deploy generative AI models on their own data, securely and with confidence.
As awareness of the potential of generative AI expands in the market, the following key considerations have emerged:


Enabling enterprise-grade control: Organizations want to enable gen AI experiences on their enterprise data, but they also want to ensure they have control over their data, so it’s not inadvertently used to train AI models without their knowledge.


Minimizing and mitigating hallucinations: Another specific gen AI risk is the potential for models to hallucinate — generate non-factual or nonsensical content.


Empowering business users: While gen AI enables numerous enterprise use cases, some of the most valuable use cases focus on enabling and empowering business users to leverage gen AI models with as little friction possible.


Scaling gen AI use cases: Enterprises need a way to harvest and operationalize their most promising use cases at scale and set up consistent best practices and controls. 


Most organizations have a low-risk tolerance when it comes to data privacy, policy, and regulatory compliance. At the same time, they don’t consider delaying gen AI adoption as a viable option due to market and competitive pressures, especially given its promise for driving transformation. As a result, Aible wanted an AI approach that a wide variety of enterprise users could adopt and adjust quickly to a rapidly evolving landscape — all while keeping customer data secure.
Aible decided to leverage Vertex AI, Google Cloud’s AI platform, to ensure customers have confidence and full control over how their data is used and accessed when developing, training, or fine-tuning AI models.

    aside_block
    

Enabling enterprise-grade controls 
Google Cloud’s design approach means customer data is secure by default on day 1 without requiring customers to take any additional actions. Google AI products and services provide security and privacy directly in your Google Cloud tenant projects. For instance, Vertex AI Agent Builder, Enterprise Search, and Conversation AI can all access and use secure customer data in Cloud Storage, which you can further secure using customer-managed encryption keys (CMEK).
Aible’s Infrastructure as Code approach allows you to leverage all of the benefits of Google Cloud directly in your own projects in a matter of minutes. The end-to-end experience is completely secured in the Vertex AI Model Garden, whether you choose Google gen AI models like Gemini, third-party models from Anthropic and Cohere, or open models, such as LLama or Gemma.







  
    
      
  

    

      
      
        
        
        
        
      
    

  
      
    
  





Aible also worked with its customer advisory board, which includes Fortune 100 companies, to design a solution that can invoke third-party gen AI models without exposing proprietary data outside of Google Cloud. Instead of sending raw data to an external model, Aible only sends high-level statistics on clusters, and this information can be masked as required. For example, it might send counts and averages based on geography or product instead of sending raw sales data. 
This leverages a privacy technique called k-anonymity, which ensures data privacy by never providing information on groups of individuals smaller than k. You can change the default setting of k; the higher the k, the more private the information transfer. If masking is applied, Aible changes the name of a variable like “Country” to “Variable A” and a value like “Italy” to “Value X,” making the data transfer even more secure. 
Mitigating hallucination risk
With gen AI, it’s critical to mitigate and reduce the risk of hallucinations using grounding, retrieval augmented generation (RAG), and other techniques. As a Built with Google Cloud AI partner, Aible provides automated analysis to augment human-in-the-loop review processes, empowering human experts with appropriate tools that can scale beyond manual efforts.







  
    
      
  

    

      
      
        
        
        
        
      
    

  
      
    
  





One of the primary ways Aible helps reduce hallucinations is by using its auto-generated Information Model (IM) — an explainable AI that double checks gen AI responses and confirms facts based on the context represented in your structured enterprise data at scale to prevent wrong decisions.







  
    
      
  

    

      
      
        
        
        
        
      
    

  
      
    
  





Aible’s Information Mode ]]></description>
<enclosure url="https://storage.googleapis.com/gweb-cloudblog-publish/images/27_-_Partners_TJbWqPw.max-2600x2600.jpg" length="49398" type="image/jpeg"/>
<pubDate>Thu, 09 Jan 2025 08:28:23 +0100</pubDate>
<dc:creator>admin</dc:creator>
<media:keywords>Generative, with, enterprise, controls, for, business, users, Hours</media:keywords>
</item>

<item>
<title>Google Cloud NetApp Volumes now available for OpenShift on Google Cloud</title>
<link>http://cloud.sitez.gr/google-cloud-netapp-volumes-now-available-for-openshift-on-google-cloud</link>
<guid>http://cloud.sitez.gr/google-cloud-netapp-volumes-now-available-for-openshift-on-google-cloud</guid>
<description><![CDATA[ As a result of new joint efforts across NetApp, Red Hat and Google Cloud, we are announcing support for Google Cloud NetApp Volumes in OpenShift on Google Cloud through NetApp Trident Version 24.10. This enables joint customers to take advantage of Google Cloud infrastructure that’s optimized for OpenShift, reduce operational toil, and streamline migration of complex workloads.
The power of OpenShift-optimized infrastructure
Red Hat and Google Cloud have a long history of collaborating on and contributing to Kubernetes as well as other Cloud Native Compute Foundation (CNCF) projects including Istio, Knative and Tekton. Together, these projects make up the basis for OpenShift, Red Hat’s platform that helps developers build, deploy, and manage applications. In fact, Google and Red Hat have been collaborating since before Kubernetes was even conceived, including co-developing Cgroups, a precursor to Linux containers. When Google open-sourced Kubernetes, Red Hat was one of the first to jump on board, betting the Red Hat OpenShift platform on the new open-source standard. Today, Google and Red Hat hold prominent leadership roles in Kubernetes governance, and are #1 and #2 largest contributors to Kubernetes, respectively.
Google Cloud infrastructure is highly optimized for OpenShift. Custom machine shapes let you optimize OpenShift Pods:Nodes bin-packing, reducing how much compute capacity you need to provision for a typical OpenShift workload. Hyperdisk Storage Pools enables thin-provisioning for OpenShift PersistentVolumes, reducing the amount of storage that needs to be provisioned. Additionally, support for live migration in a wide array of Compute Engine families lets you provide higher uptime guarantees for stateful OpenShift workloads, which are common in enterprise application portfolios.
And when you deploy OpenShift workloads on Google Cloud, you can count on access to a deep bench of L3/ L4 engineers who are experts in the OpenShift runtime core components (in Kubernetes) given Google’s staff strong participation as core maintainers and technical leads in Kubernetes, providing you with enterprise-grade support and coverage for mission-critical workloads.

    aside_block
    

NetApp Volumes storage comes to OpenShift on Google Cloud
When you deploy OpenShift workloads on Google Cloud, there’s a wide array of options for modernizing your operations, with OpenShift-native integrations into managed infrastructure services across compute, networking, storage, monitoring/logging, secrets/encryption, serverless, CI/CD, etc.
These managed infrastructure services give you the ability to “carry much fewer pagers” than you typically would with an on-prem OpenShift deployment. However, sometimes you are migrating applications that have requirements or dependencies on specific solutions for infrastructure pillars (such as storage). The typical approach is to rely on self-managing the architecture — and going back to carrying pagers…
With support for Google Cloud NetApp Volumes in OpenShift, you benefit from the best of both worlds for your file storage needs: the modernization, toil-reduction, and efficiency benefits of a managed service, with enterprise-ready features, compatibility, and familiarity of NetApp on-premises storage.
You can maximize data performance and reliability for your Red Hat OpenShift workloads on Google Cloud by leveraging high-performance file storage on Google Cloud infrastructure while using NetApp Volumes features like automated snapshots, and Red Hat OpenShift-native persistent storage integration helps ensure high availability and fault tolerance across your workloads. 
Streamlined deployment for a variety of workloads
Collaboration between Google Cloud, NetApp and Red Hat makes it easier to quickly configure and deploy Red Hat OpenShift clusters and workloads in Google Cloud with NetApp Volumes for file storage, with streamlined access to Google Cloud IAM, service account management, and the Certificate Authority Service, among others. NetApp Volumes provides as small as 1 GiB volumes, read-write many (RWX) PVs, low-latency performance and up to 12.5 GiB/sec throughput with large volumes, all while protecting your applications and data with customer managed encryption keys (CMEK). 
“Google Cloud is heavily invested in our partner community with the common goal of providing a world-class experience for our customers. Building on our long-standing technical collaborations with industry-leading partners like Red Hat and NetApp, we are deeply aligned on the core principles of both openness and reliability to help enterprise customers get what they need done. Our customers are increasingly turning to us to help them transform their business and together, and through a joint partnership with NetApp and Red Hat, we can help customers with a new way to cloud, while leveraging familiarity and consistency that brings together innovations across their business.” - Stephen Orban, Google Cloud VP ]]></description>
<enclosure url="https://storage.googleapis.com/gweb-cloudblog-publish/images/27_-_Partners_TJbWqPw.max-2600x2600.jpg" length="49398" type="image/jpeg"/>
<pubDate>Thu, 09 Jan 2025 08:28:20 +0100</pubDate>
<dc:creator>admin</dc:creator>
<media:keywords>Google, Cloud, NetApp, Volumes, now, available, for, OpenShift, Google, Cloud</media:keywords>
</item>

<item>
<title>Build, deploy, and promote AI agents through Google Cloud’s AI agent ecosystem</title>
<link>http://cloud.sitez.gr/build-deploy-and-promote-ai-agents-through-google-clouds-ai-agent-ecosystem</link>
<guid>http://cloud.sitez.gr/build-deploy-and-promote-ai-agents-through-google-clouds-ai-agent-ecosystem</guid>
<description><![CDATA[ We’ve seen a sharp rise in demand from enterprises that want to use AI agents to automate complex tasks, personalize customer experiences, and increase operational efficiency. Today, we’re announcing a Google Cloud AI agent ecosystem program to help partners build and co-innovate AI agents with technical and go-to-market resources from Google Cloud. We’re also launching AI Agent Space, a new category in our Google Cloud Marketplace for customers to easily find and deploy partner-built AI agents. 
Through this program, we’ll provide product support, marketing amplification, and co-selling opportunities to help our services and ISV partners bring these solutions to market faster, reach more customers, and grow their AI agent businesses. Our goal is to provide customers with a rich ecosystem of solutions that sit on top of our world-class infrastructure and offer the choice and optionality needed to tailor AI for their businesses and maximize value from AI investments. 

    aside_block
    

New resources for partners building AI Agents
To increase the development and adoption of AI agents, we’re focusing on supporting partners in three key areas:

Accelerated agent development: We&#039;ll provide partners with direct access to Google Cloud&#039;s product and engineering teams for guidance and optimization of their AI agents. Partners will also receive early access to our latest AI technologies, technical enablement and best practices, and dedicated support for bringing their solutions to market quickly via Google Cloud Marketplace.
Go-to-market success: New go-to-market programs and co-selling opportunities specifically designed for AI agent solutions will help partners more effectively promote their offerings and drive adoption across a wider range of customers.
Increased customer visibility: We will highlight the innovative work of our partners through targeted marketing resources, blogs, and dedicated events, which will increase visibility of partner-built AI agents and help them stand out in our growing AI ecosystem.

Offerings from services partners
We’ve seen significant momentum from services partners who have used Google Cloud’s technology to help customers successfully build and deploy AI agents. Through this program, our services partners will make their AI agents available to even more customers, including on AI Agent Space in the future. Here are some of their innovative agent solutions: 

Accenture is transforming customer support at a major retailer by offering convenient self-service options through virtual assistants, enhancing the overall customer experience.
Bain supports SEB’s wealth management division with an AI agent that enhances end-customer conversations with suggested responses and generates call summaries that help increase efficiency by 15%.  
BCG provides a sales optimization tool to improve the effectiveness and impact of insurance advisors. 
Capgemini optimizes the ecommerce experience by helping retailers accept customer orders through new revenue channels and to accelerate the order-to-cash process for digital stores.
Cognizant helps legal teams draft contracts, assigning risk scores and recommendations for how to optimize operational impact.  
Deloitte offers a “Care Finder” agent as part of its Agent Fleet, helping care seekers find in-network providers often in less than a minute — significantly faster than the average call time of 5-8 minutes.
HCLTech helps predict and eliminate different types of defects on manufacturing products with its manufacturing quality agent, Insight.
Infosys optimizes digital marketplaces for a leading consumer brand manufacturer, providing actionable insights on inventory planning, promotions, and product descriptions. 
PwC uses AI agent technology to help oncology clinics streamline administrative work so that doctors can optimize their time with patients.
TCS helps build persona-based AI agents contextualized with enterprise knowledge to accelerate software development.
Wipro supports a national healthcare provider in using agent technology to develop and adjust contracts, streamlining a complex and time-consuming task while improving accuracy. 

Partners have already given us positive feedback about the support we’ve provided to more effectively scale their agent solutions, including Datatonic, Kyndryl, Quantiphi, and Slalom who plan to bring new agents to market soon. Here’s what partners had to say:

“Leaders who prioritize and invest in agentic architecture will be at the forefront of their industries, driving future growth with generative AI. For example, Accenture&#039;s marketing team is using autonomous agents to streamline campaign creation and execution, reducing manual steps by 25-35%, saving 6% in costs, and speeding up time-to-market by 25-55%.” - Scott Alfieri, Global Lead, Google Business Group, Accenture
“BCG continues to see strong business value partnering with Google Cloud to deliver gen AI transformations for our joint clients across ind ]]></description>
<enclosure url="https://storage.googleapis.com/gweb-cloudblog-publish/images/27_-_Partners_TJbWqPw.max-2600x2600.jpg" length="49398" type="image/jpeg"/>
<pubDate>Thu, 09 Jan 2025 08:28:17 +0100</pubDate>
<dc:creator>admin</dc:creator>
<media:keywords>Build, deploy, and, promote, agents, through, Google, Cloud’s, agent, ecosystem</media:keywords>
</item>

<item>
<title>Registration is open for Partner Summit at Google Cloud Next</title>
<link>http://cloud.sitez.gr/registration-is-open-for-partner-summit-at-google-cloud-next</link>
<guid>http://cloud.sitez.gr/registration-is-open-for-partner-summit-at-google-cloud-next</guid>
<description><![CDATA[ Partner Summit at Google Cloud Next is returning April 8–11, 2025, in Las Vegas! 
Our last event was full of highlights, and we’ve got even more in store for partners in 2025. 
Based on your feedback, Partner Summit 2025 will begin on Tuesday, April 8 – one day before Google Cloud Next kicks off – to offer a dedicated day of partner breakout sessions and learning opportunities before the main event begins. The Partner Summit Lounge, partner keynote, lightning talks, and more will all be available April 9–11, 2025. 
Partner Summit is your exclusive opportunity to: 

Accelerate your business by aligning on joint business goals, learning about new programmatic and incentive opportunities, and diving deep into cutting-edge insights in our Partner Summit breakout sessions and lightning talks.
Build new connections as you network with other partners and Googlers while you explore the activities and perks located in our exclusive Partner Summit Lounge.
Get a look at what’s next from Google Cloud leadership at the dedicated partner keynote to learn about where cloud is headed – and how our partners are central to our mission.
Make the most of our partnership with personalized advice from Google Cloud team members on incentives, certifications, co-marketing, and more at our Meet the Experts booths.

Get ready to learn, connect, and build the future of business with us. Early bird registration is now open for $999. This special rate is only available through February 14, 2025, or until tickets are sold out. 
Register now ]]></description>
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<pubDate>Thu, 09 Jan 2025 08:28:13 +0100</pubDate>
<dc:creator>admin</dc:creator>
<media:keywords>Registration, open, for, Partner, Summit, Google, Cloud, Next</media:keywords>
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<item>
<title>Build agentic RAG on Google Cloud databases with LlamaIndex</title>
<link>http://cloud.sitez.gr/build-agentic-rag-on-google-cloud-databases-with-llamaindex</link>
<guid>http://cloud.sitez.gr/build-agentic-rag-on-google-cloud-databases-with-llamaindex</guid>
<description><![CDATA[ AI agents are revolutionizing the landscape of gen AI application development. Retrieval augmented generation (RAG) has significantly enhanced the capabilities of large language models (LLMs), enabling them to access and leverage external data sources such as databases. This empowers LLMs to generate more informed and contextually relevant responses. Agentic RAG represents a significant leap forward, combining the power of information retrieval with advanced action planning capabilities. AI agents can execute complex tasks that involve multiple steps that reason, plan and make decisions, and then take actions to execute goals over multiple iterations. This opens up new possibilities for automating intricate workflows and processes, leading to increased efficiency and productivity.
LlamaIndex has emerged as a leading framework for building knowledge-driven and agentic systems. It offers a comprehensive suite of tools and functionality that facilitate the development of sophisticated AI agents. Notably, LlamaIndex provides both pre-built agent architectures that can be readily deployed for common use cases, as well as customizable workflows, which enable developers to tailor the behavior of AI agents to their specific requirements. 
Today, we&#039;re excited to announce a collaboration with LlamaIndex on open-source integrations for Google Cloud databases including AlloyDB for PostgreSQL and Cloud SQL for PostgreSQL.
These LlamaIndex integrations, available to download via PyPi llama-index-alloydb-pg and  llama-index-cloud-sq-pg, empower developers to build agentic applications that can connect with Google databases. The integrations include:










Integrations


Description 


Link to documentation on GitHub




LlamaIndex Vector Store


Stores vector embeddings of the content and retrieves semantically similar vectors to queries


AlloyDB , Cloud SQL for PostgreSQL 




LlamaIndex Document Store


Stores the content related to the vectors in the vector store


AlloyDB , Cloud SQL for PostgreSQL 




LlamaIndex Index Store


Stores metadata about the content in your document store


AlloyDB , Cloud SQL for PostgreSQL 










In addition, developers can also access previously published LlamaIndex integrations for Firestore, including for Vector Store and Index Store.
Integration benefits
LlamaIndex supports a broad spectrum of different industry use cases, including agentic RAG, report generation, customer support, SQL agents, and productivity assistants. LlamaIndex&#039;s multi-modal functionality extends to applications like retrieval-augmented image captioning, showcasing its versatility in integrating diverse data types. Through these use cases, joint customers of LlamaIndex and Google Cloud databases can expect to see an enhanced developer experience, complete with:


Streamlined knowledge retrieval: Using these packages makes it easier for developers to build knowledge-retrieval applications with Google databases. Developers can leverage AlloyDB and Cloud SQL vector stores to store and semantically search unstructured data to provide models with richer context. The LlamaIndex vector store integrations let you filter metadata effectively, select from vector similarity strategies, and help improve performance with custom vector indexes.


Complex document parsing: LlamaIndex’s first-class document parser, LlamaParse, converts complex document formats with images, charts and rich tables into a form more easily understood by LLMs; this produces demonstrably better results for LLMs attempting to understand the content of these documents.


Secure authentication and authorization: LlamaIndex integrations to Google databases utilize the principle of least privilege, a best practice, when creating database connection pools, authenticating, and authorizing access to database instances.


Fast prototyping: Developers can quickly build and set up agentic systems with  readily available pre-built agent and tool architectures on LlamaHub.


Flow control: For production use cases, LlamaIndex Workflows provide the flexibility to build and deploy complex agentic systems with granular control of conditional execution, as well as powerful state management.



    aside_block
    

A report generation use case
Agentic RAG workflows are moving beyond simple question and answer chatbots. Agents can synthesize information from across sources and knowledge bases to generate in-depth reports. Report generation spans across many industries — from legal, where agents can do prework such as research, to financial services, where agents can analyze earning call reports. Agents mimic experts that sift through information to generate insights. And even if agent reasoning and retrieval takes several minutes, automating these reports can save teams several hours.
LlamaIndex provides all the key components to generate reports:


Structured output definitions with the ability to organize outputs into Report templates


Intelligent document ]]></description>
<enclosure url="https://storage.googleapis.com/gweb-cloudblog-publish/images/10_-_Databases.max-2600x2600.jpg" length="49398" type="image/jpeg"/>
<pubDate>Thu, 09 Jan 2025 08:28:13 +0100</pubDate>
<dc:creator>admin</dc:creator>
<media:keywords>Build, agentic, RAG, Google, Cloud, databases, with, LlamaIndex</media:keywords>
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<item>
<title>How Fullstory uncovers user insights with Vertex AI serving Gemini 1.5 Pro</title>
<link>http://cloud.sitez.gr/how-fullstory-uncovers-user-insights-with-vertex-ai-serving-gemini-15-pro</link>
<guid>http://cloud.sitez.gr/how-fullstory-uncovers-user-insights-with-vertex-ai-serving-gemini-15-pro</guid>
<description><![CDATA[ Mapping the user experience is one of the most persistent challenges a business can face. Fullstory, a leading behavioral data analytics platform, helps organizations identify pain points and optimize digital experiences by reproducing  user sessions and sharing strong analytics highlighting areas for improvement in the customer&#039;s journey. This boosts conversion rates, reduces churn, and enhances customer satisfaction. 
AI has made this even stronger. Fullstory&#039;s comprehensive AI-powered autocapture technology, Fullcapture, removes the need for manual instrumentation and uncovers hidden patterns that might otherwise be missed. 
Today, we’ll share how Fullstory leverages Vertex AI serving Gemini 1.5 Pro to strengthen their autocapture technology.

    aside_block
    

How Vertex AI and AI agents help Fullstory measure the user experience
Think of Fullcapture as a video recorder for your website or app, capturing every interaction in detail. Traditional autocapture methods are more like transcription services, logging only selected highlights and often missing the complete picture. With Fullcapture, no user action goes unrecorded, with minimal impact on device performance. Operating server-side, Fullcapture allows for revisiting any aspect of user behavior as needed. If a new signal is required, it can be easily retrieved from the recorded data without affecting client-side performance.
The table below breaks down how Fullcapture goes beyond traditional autocapture capabilities to give users a deeper understanding of their customer data.







  
    
      
  

    

      
      
        
        
        
        
      
    

  
      
    
  





By integrating its Fullcapture capabilities with Google&#039;s Vertex AI serving Gemini 1.5 Pro, Fullstory empowers customers to effortlessly analyze this extensive data and focus on what truly matters. Driven by a proactive AI agent, Fullstory enables faster data discovery by highlighting important elements and automatically categorizing user interactions into semantic components, providing even deeper levels of analysis.
AI-powered data discovery 
Data discovery is a 6-step process that involves exploring, classifying, and analyzing data from various sources to uncover patterns and extract actionable insights. This process allows users to visually navigate data relationships and apply advanced analytics to optimize business decisions and performance. 







  
    
      
  

    

      
      
        
        
        
        
      
        Mountain visual with six flags that represent the steps for data discovery: Set goals, aggregate, prepare, visualize, analyze, and repeat.
      
    

  
      
    
  





To effectively analyze user behavior, businesses need to identify and label key elements on their websites (e.g., buttons, forms). This process can be tedious and time-consuming. Fullstory’s AI agent, powered by Gemini 1.5 Pro, automates this critical task by scraping data from user interactions and making intelligent decisions at various stages—identifying key elements, determining their significance, and autonomously categorizing them. This multi-stage decision-making process not only streamlines workflows but also ensures businesses can focus on deriving actionable insights rather than manual labeling.
Within Fullstory, &quot;elements&quot; allow users to label UI components based on specific CSS selectors. A CSS selector is a pattern used to target elements in a webpage, such as classes, IDs, or attributes. For instance, a &quot;Checkout Button&quot; element might be created with the selector .checkout-page-container [data-testid=&quot;primary-button&quot;]. These labels help categorize UI components and utilize them for product analytics. Broad semantic labeling is crucial for long-term success with Fullstory, and automating this process simplifies workflows for users.







  
    
      
  

    

      
      
        
        
        
        
      
        A heatmap in Fullstory displaying the most clicked Elements. On the right hand side, the Elements “Site Logo” and “[JF] Product Pic” are configured Elements.
      
    

  
      
    
  





Vertex AI with Gemini 1.5 Pro offers a unique opportunity to add a human touch at scale. It proactively identifies and describes web components, ultimately providing actionable insights that benefit Fullstory customers. Gemini 1.5 Pro is trained on extensive web expertise, including web implementation from CSS and web frameworks like React, along with a vast dataset of website images.







  
    
      
  

    

      
      
        
        
        
        
      
    

  
      
    
  





For example, the model can analyze a website screenshot and accurately describe its components, understanding both the overall structure, visible text, and the logical structure of the web page. This understanding can be further enhanced with web implementation details, such as CSS selectors, to gain a deeper understanding of  ]]></description>
<enclosure url="https://storage.googleapis.com/gweb-cloudblog-publish/images/27_-_Partners_TJbWqPw.max-2600x2600.jpg" length="49398" type="image/jpeg"/>
<pubDate>Thu, 09 Jan 2025 08:28:09 +0100</pubDate>
<dc:creator>admin</dc:creator>
<media:keywords>How, Fullstory, uncovers, user, insights, with, Vertex, serving, Gemini, 1.5, Pro</media:keywords>
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<item>
<title>Achieve peak SAP S/4HANA performance with Compute Engine X4 machines</title>
<link>http://cloud.sitez.gr/achieve-peak-sap-s4hana-performance-with-compute-engine-x4-machines</link>
<guid>http://cloud.sitez.gr/achieve-peak-sap-s4hana-performance-with-compute-engine-x4-machines</guid>
<description><![CDATA[ Enterprise workloads like SAP S/4HANA present unique challenges when migrating to a public cloud, making the choice of a cloud provider critically important. As an in-memory database for large SAP deployments, SAP HANA can have massive memory and CPU processing requirements, and that can just be the beginning of the challenges. Businesses are generating, processing, and making business decisions on more data than ever before, and they require increasingly complex data analysis, algorithms, and reporting. SAP provides the real-time operational and historical analytics and insights businesses need to make informed decisions quickly. However, this requires machines that can process and analyze large volumes of data in real time, demanding high levels of memory and compute power. 
To meet these demands, we recently introduced X4 machine types. The Compute Engine X4 machine family is purpose-built to handle the demanding requirements of SAP HANA Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) workloads. These machines deliver strong performance, scalability, and reliability, empowering businesses to unlock the full potential of their SAP S/4HANA, SAP Business Suite (ECC) on SAP HANA deployments, and SAP’s Industry Solutions. X4 is also built to support OLAP workloads such as SAP BW/4HANA and SAP BW on HANA.

    aside_block
    

Uncompromising performance in a scalable solution
As businesses grow, whether organically or via acquisitions, they often risk hitting CPU and memory limitations of their SAP HANA system. This forces them to upgrade to larger scale-up machines earlier or using multiple machines in a scale-out configuration. Scaling out is a complex custom process that requires application data redesign and approvals from SAP, as well as being costly from an operational and resource perspective. In addition, the planned downtime required for these migration operations can be unacceptable for organizations that rely on SAP for mission-critical operations.
X4 solves these challenges by allowing businesses to grow further in scale-up mode before hitting performance bottlenecks. X4 is available in 16TB, 24TB, and 32TB memory configurations and 960, 1440, 1920 vCPU cores respectively with “standard sizing” SAP certification for both SAP HANA OLTP and OLAP capable of running the most demanding enterprise SAP HANA workloads. X4 machines also boast scale-out OLTP certifications for up to four nodes the size of the servers, for a total of 128 terabytes for S/4HANA use cases — larger than any other cloud provider’s. The X4 16TB machines achieved an SAP Benchmark IaaS SAPS result that is over 8% higher than the closest IaaS cloud’s. (Source: SAP note 2456432 - Logon required). 
As the only cloud IaaS provider offering a 32TB SAP-certified machine, Google Cloud helps ensure that your SAP HANA environment grows seamlessly with your business needs while providing a cost-effective and simple cloud-native experience. Here’s how:


Industry-leading compute and storage: The Google Cloud X4 machine family gains its power and scale through a combination of powerful hardware and software optimizations. Integration with Google Cloud Hyperdisk provides high performance, scalability, and resilient block storage. Hyperdisk provides dedicated, dynamically tunable read/write operations per second (IOPS) and throughput with each volume up to 5GB per second, delivering substantially higher performance compared to the previous generation of Persistent Disk. This level of performance is crucial for applications that require rapid data access, like databases, real-time analytics, and reduces SAP HANA rehydration times. Additionally, X4 machines run on one of the world’s largest privately managed networks, offering low-latency performance. 


High availability and reliability: Minimizing downtime for mission-critical SAP applications is a top priority for customers. X4 machines have an industry-leading 99.95% Compute Engine Memory-Optimized Single Instance SLA. Paired with Hyperdisk, X4 enables rapid SAP HANA rehydration capabilities ( ]]></description>
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<pubDate>Thu, 09 Jan 2025 08:28:04 +0100</pubDate>
<dc:creator>admin</dc:creator>
<media:keywords>Achieve, peak, SAP, S4HANA, performance, with, Compute, Engine, machines</media:keywords>
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<title>Google Cloud expands its support for founders through partnerships with 300 accelerators worldwide</title>
<link>http://cloud.sitez.gr/google-cloud-expands-its-support-for-founders-through-partnerships-with-300-accelerators-worldwide</link>
<guid>http://cloud.sitez.gr/google-cloud-expands-its-support-for-founders-through-partnerships-with-300-accelerators-worldwide</guid>
<description><![CDATA[ At Google Cloud, we focus on building the most competitive and powerful network of support for startups. One of the ways we show our support is by partnering with investors, accelerators, and incubators to deliver the resources and benefits that help startups succeed.
For example, we are proud to partner with marquee institutions who invest in the next generation of founders like Y Combinator. We have also extended our network of partnerships to accelerators worldwide who support founders with mentorship, education, and in some cases, investment, such as ERA and AI2 Incubator.
In 2024, we worked with over 300 accelerators worldwide to help thousands of startups and over 3,000 founders build with Google. We&#039;ve extended benefits to these startups including access to Startup Success Managers, Customer Engineers, and AI product teams, dedicated packages of credits, and technical programming like workshops and office hours.
Today, we’re proud to announce our latest partnerships with three more accelerators – Berkeley SkyDeck, Upekkha, and UnternehmerTUM – and highlight some of the companies we’re supporting through them.

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Introducing our latest accelerator partnerships
Berkeley SkyDeck is the only university accelerator partnering with a leading venture capital fund. Berkeley’s mission emphasizes long-term societal benefit, and prioritizes companies that align with this vision. Several SkyDeck companies are already running on Google Cloud, including:


Deeli AI, an AI-powered platform that helps companies discover and evaluate emerging technologies to make informed investment decisions. They currently build their product and data pipeline on various services such as GCE, Cloud Run, and Dataflow, and interact with models from the Vertex AI Model Garden.


ContextQA is Agentic AI for software testing, providing 12x the value by enabling accurate, user-centric test automation from day zero of development and helps to deliver bug-free product 40% faster. ContextQA uses Gemini models to continuously compare actual application behavior with expected behavior, adapting automatically to new changes for immediate agility.


T-Robotics provides pre-trained AI skills for robots that make commercial robots intelligent and robust.  These skills are programmed through a conversational robot agent that leverages visual, haptic, action and language models - including Google Cloud&#039;s Gemini - to seamlessly interpret and adapt to diverse industrial environments.


“Our partnership with Google Cloud enables startups to build better and faster, which is crucial for their success. Beyond the technology and services provided, we foster meaningful connections between our startups and Googlers, facilitating discussions on industry trends and innovations in AI.” – Taylor Marcus, Head of Business Development at Berkeley Skydeck
Upekkha helps Indian founders build vertical AI companies that sell globally, with intense coaching, a network of founders, and capital. Google Cloud is partnering with them to support:


Outpost is a platform for AI/ML and data teams to train, fine tune, and deploy genAI models with managed infrastructure, tools, and workflows.


Labellerr&#039;s data labeling engine uses automated annotation, and smart QA, processing millions of images and thousands of hours of videos in just a few weeks using Google Vertex AI Integration and Cloud Run, which previously took months for ML teams.


Bynry&#039;s SMART360 leverages Google Cloud’s robust infrastructure to empower small and mid-sized utilities to enhance operational efficiency and customer satisfaction. 


“Google Cloud has technology that just works. You can tell they actually listen to developers. They don’t just give out credits; they help founders understand how to use their technology.” – Thiyagarajan Maruthavanan (Rajan) - Managing Partner, Upekkha
UnternehmerTUM is the leading center for innovation and business creation in Europe with more than 50 high-growth technology start-ups every year, and offers complete service from initial idea to IPO. Startups supported by them include:


Kraftblock’s innovative technology offers unparalleled large-scale, long-duration energy storage, empowering industries to transition towards sustainable thermal processes. The green tech company is using Google’s Compute Engine to power their simulations.


tulanā’s highly customizable platform uses forecasting, optimization, simulation and AI to help enterprise clients take better decisions across their supply chains. tulanā is using Google Cloud Run to horizontally scale its optimization workloads, Google&#039;s Gemini model for intelligent ETL processes, and Cloud SQL and Big Query to store customer data.


SE3 Labs specializes in 3D computer vision and AI. They develop advanced technologies to create &quot;Spatial GPTs,&quot; which are essentially AI models that can understand and interact with the world in 3D. The startup loves using Google Cloud Run for their deployment.


“We ch ]]></description>
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<title>Google Cloud and SAP: Powering AI with enterprise data</title>
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<description><![CDATA[ As the 2027 end of support for SAP Business Suite 7 approaches, SAP customers need to decide where to deploy as they upgrade to cloud-based S/4HANA and RISE with SAP. This represents a great opportunity to get more value out of your enterprise data, and take advantage of the longstanding partnership between Google Cloud and SAP. RISE with SAP on Google Cloud lets you accelerate innovation and reduce costs through a secure, reliable, and scalable high-performance infrastructure. By deploying SAP on Google Cloud, you can accelerate your ERP transformation with a unique, unified data experience that offers leading integration, analytics accelerators, and AI innovations to bring clarity from data to decisions.
Two data powerhouses unite
Key to the Google Cloud-SAP partnership is the integration between BigQuery, a fully managed, AI-ready data analytics platform that helps you maximize value from your data, and SAP Datasphere. BigQuery is designed to be multi-engine, multi-format, and multi-cloud. This integration lets you access your most critical data in real time without duplication thanks to co-engineered data replication and federation technologies. This joint capability can unify data from SAP software systems, such as SAP S/4HANA and SAP HANA Cloud, providing organizations with a comprehensive view of their most important data on Google Cloud and letting you:


Simplify data landscapes. Federate queries across SAP Datasphere and BigQuery to blend data from SAP and non-SAP software. This minimizes common data silos from sources that span marketing, sales, purchasing, finance, supply chain, manufacturing and more. 


Improve decision-making. Gain a more holistic view of your data and leverage powerful analytics to drive better decision-making. Now, you can plan with a single, comprehensive view of your businesses by connecting SAP data to powerful data and analytics tools such as Vertex AI to analyze financial and business outcomes while improving the accuracy of models. 


Add rich context to your analytics. BigQuery makes it easy to bring in Google and third-party data sources such as Trends, Weather, Maps, world events and Enterprise application data with Google Cloud Cortex Framework’s pre-built accelerators. 


Use SAP Business Technology Platform (SAP BTP) globally on Google Cloud. SAP is advancing its multi-cloud offerings by expanding regional support of SAP BTP and SAP HANA Cloud on Google Cloud, which includes support for SAP Analytics Cloud and SAP Datasphere. SAP and Google Cloud intend to launch SAP BTP in five new regions, building to a total of eight regions supported by 2025.



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Transform your business with AI
Google Cloud has powerful, easy-to-use tools to help you incorporate your SAP data into predictive and generative AI initiatives. Vertex AI provides a single, integrated development platform where you can build sophisticated predictive and generative AI agents and experiences faster — without any manual ETL.


Innovate freely with Model Garden, a curated collection of over 150 machine learning models. Google is the only cloud provider to offer widely used first-party, third-party, and open-source models, so you can easily discover and choose foundation models based on modality, size, performance, latency, and cost. 


Leverage Google’s most powerful model yet. Gemini is Google’s most capable and general family of LLMs, offering four models each built for its own set of use cases. Google Cloud recently announced multiple Gemini updates that enable SAP users to bring more gen AI capabilities to SAP workloads and access Google Cloud’s large language models through SAP GenAI Hub.


Get more from your models by augmenting and grounding them with SAP data. Leverage Vertex AI Model Builder’s managed tooling for extensions, function calling, and grounding. Customize Gemini models in an efficient, lower-cost way with supervised tuning.


Easily deploy, manage, and monitor agents with Vertex AI Agent Builder, which lets you quickly create a range of generative AI agents grounded with Google Search and your SAP data with the convenience of a no-code agent builder console. 


Run SAP today on tomorrow’s infrastructure
Google Cloud helps SAP customers build quickly, securely, and cost effectively with the next generation of infrastructure designed to meet specific workload and industry needs. Google Cloud infrastructure is optimized for AI, cloud-first, enterprise workloads with an emphasis on security, scalability, and data sovereignty. 


Leverage superior scale-up machine types. Google Cloud is the sole provider of 32TB SAP-certified machines, simplifying architecture, deployments, and administration. More recently, Google Cloud introduced memory-optimized X4 instances, which provide the largest SAP-certified compute instances in the cloud market, supporting up to 32TB SAP HANA workloads.


Accelerate time to value. Google Cortex for SAP Framework delivers pre-built analyti ]]></description>
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<media:keywords>Google, Cloud, and, SAP:, Powering, with, enterprise, data</media:keywords>
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