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

AWS Machine Learning - AI

In this post, we explore the new Container Caching feature for SageMaker inference, addressing the challenges of deploying and scaling large language models (LLMs). You’ll learn about the key benefits of Container Caching, including faster scaling, improved resource utilization, and potential cost savings.

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AI-based Travel Assistant Wins AltexSoft’s GenAI Hackathon

Altexsoft

It helped engineers, managers, and admin staff learn large language models (LLMs) capabilities and train at building products based on LLM APIs. Besides the hackathon, the Month of AI included webinars on OpenAI SDKs, LLM agents, and prompt techniques.

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How AWS sales uses Amazon Q Business for customer engagement

AWS Machine Learning - AI

By moving our core infrastructure to Amazon Q, we no longer needed to choose a large language model (LLM) and optimize our use of it, manage Amazon Bedrock agents, a vector database and semantic search implementation, or custom pipelines for data ingestion and management.

AWS 110
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10 ways AI can make IT more productive

CIO

When it comes to maximizing productivity, IT leaders can turn to an array of motivators, including regular breaks, free snacks and beverages, workspace upgrades, mini contests, and so on. Yet there’s now another, cutting-edge tool that can significantly spur both team productivity and innovation: artificial intelligence.

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Compass Tech Summit: A Remarkable 5-In-1 Tech Conference

Apiumhub

Compass Tech Summit: 5-in-1 Conferences Reinforce Reinforce is an international Artificial intelligence and Machine Learning hybrid conference as part of the Compass Tech Summit. The featured speakers also include experts in the field, from CEOs to data engineering managers and senior software engineers.

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Use LangChain with PySpark to process documents at massive scale with Amazon SageMaker Studio and Amazon EMR Serverless

AWS Machine Learning - AI

From deriving insights to powering generative artificial intelligence (AI) -driven applications, the ability to efficiently process and analyze large datasets is a vital capability. He focuses on helping customers build, train, deploy and migrate machine learning (ML) workloads to SageMaker.

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Improve the productivity of your customer support and project management teams using Amazon Q Business and Atlassian Jira

AWS Machine Learning - AI

Users such as support engineers, project managers, and product managers need to be able to ask questions about a project, issue, or customer in order to provide excellence in their support for customers’ needs. For a full list of Amazon Q Business supported data source connectors, see Amazon Q Business connectors.