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The AI Future According to Google Cloud Next ’25: My Interesting Finds

Xebia

Thinking refers to an internal reasoning process using the first output tokens, allowing it to solve more complex tasks. Native Multi-Agent Architecture: Build scalable applications by composing specialized agents in a hierarchy. BigFrames provides a Pythonic DataFrame and machine learning (ML) API powered by the BigQuery engine.

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Automate Amazon Bedrock batch inference: Building a scalable and efficient pipeline

AWS Machine Learning - AI

Refer to Supported Regions and models for batch inference for current supporting AWS Regions and models. To address this consideration and enhance your use of batch inference, we’ve developed a scalable solution using AWS Lambda and Amazon DynamoDB. Select the created stack and choose Delete , as shown in the following screenshot.

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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

Software-as-a-service (SaaS) applications with tenant tiering SaaS applications are often architected to provide different pricing and experiences to a spectrum of customer profiles, referred to as tiers. The user prompt is then routed to the LLM associated with the task category of the reference prompt that has the closest match.

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How Machine Learning is Used in Finance and Banking

Exadel

The banking landscape is constantly changing, and the application of machine learning in banking is arguably still in its early stages. Machine learning solutions are already rooted in the finance and banking industry. Machine learning solutions are already rooted in the finance and banking industry.

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Model customization, RAG, or both: A case study with Amazon Nova

AWS Machine Learning - AI

Model customization refers to adapting a pre-trained language model to better fit specific tasks, domains, or datasets. Refer to Guidelines for preparing your data for Amazon Nova on best practices and example formats when preparing datasets for fine-tuning Amazon Nova models.

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Supercharge your auto scaling for generative AI inference – Introducing Container Caching in SageMaker Inference

AWS Machine Learning - AI

We then guide you through getting started with Container Caching, explaining its automatic enablement for SageMaker provided DLCs and how to reference cached versions. It addresses a critical bottleneck in the deployment process, empowering organizations to build more responsive, cost-effective, and scalable AI systems.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning - AI

Shared components refer to the functionality and features shared by all tenants. Refer to Perform AI prompt-chaining with Amazon Bedrock for more details. Additionally, contextual grounding checks can help detect hallucinations in model responses based on a reference source and a user query.