Favorite Teams benchmarking generative AI models often evaluate dozens of GPU instance types, serving containers, parallelism strategies, and optimization techniques such as speculative decoding before deploying to production. Practitioners can spend weeks navigating configuration decisions and manually piecing together what they tried, what worked, and why. That complexity is exactly
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Shared by AWS Machine Learning July 8, 2026
Favorite Every finance professional knows the drill. Monday morning arrives, and your Financial Planning and Analysis (FP&A) team disappears into data compilation. They pull numbers from multiple systems, reconcile sources, build charts, and write commentary. All to answer a question that should be straightforward: what happened with revenue last week,
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Shared by AWS Machine Learning July 8, 2026
Favorite Managing AWS infrastructure often means switching between consoles, searching documentation, and manually creating support cases. For each incident, an engineer opens the AWS Management Console, checks Amazon CloudWatch, searches AWS documentation, reviews community posts, and files a support case. This context-switching adds up to 30–45 minutes per investigation before
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Shared by AWS Machine Learning July 8, 2026
Favorite The effectiveness and accuracy of machine learning (ML) models decreases almost as soon as the training job finishes. Changes in consumer behavior, releases of new products, upgrades in sensor technology, and a shifting economic and political landscape are all examples of uncontrollable factors that change the patterns and probabilities
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Shared by AWS Machine Learning July 8, 2026
Favorite Building an AI agent that edits images based on natural language requires an orchestration loop, tool routing, memory management, and a compute environment to run it all. Amazon Bedrock AgentCore harness handles that entire stack with configuration. You declare what the agent does, and the harness runs it in
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Shared by AWS Machine Learning July 8, 2026
Favorite Amazon Quick is an AI-powered unified intelligence service that connects structured data and unstructured enterprise content so teams can explore, analyze, and act from one place. Amazon Quick Sight, the business intelligence (BI) capability within Amazon Quick, delivers interactive dashboards, natural language querying, pixel-perfect reports, machine learning (ML)-driven insights,
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Shared by AWS Machine Learning July 8, 2026
Favorite Note: The topics referenced throughout this document refer to the new Topics experience (not legacy Topics). For details on the differences, see Build a unified semantic layer across datasets with multi-dataset Topics in Amazon Quick. Most real-world business questions span multiple tables. A retailer who wants to understand net
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Shared by AWS Machine Learning July 8, 2026
Favorite In Part 1 of this series, we introduced Amazon Quick Sight Multi-Dataset Relationships and covered the foundational concepts of dimensional modeling, best practices for designing clean data models, and a decision framework for when to use runtime joins versus pre-joined datasets. If you haven’t read Part 1 yet, we
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Shared by AWS Machine Learning July 8, 2026
Favorite Business intelligence analysts routinely face the same challenge at the start of every analytics project: the data needed to answer a single business question lives across multiple tables. Sales transactions sit in one place, customer demographics and product attributes in another, while returns, forecasts, and operational metrics occupy still
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Shared by AWS Machine Learning July 8, 2026
Favorite If you’ve been managing Amazon Quick legacy Topics alongside your datasets, you know the challenge: two assets that must stay perfectly synchronized, each with its own permissions, lineage, and versioning. Column synonyms drift. Calculated fields diverge. A rename in the dataset breaks the Legacy Topic silently. You can now
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Shared by AWS Machine Learning July 8, 2026
Shared by AWS Machine Learning July 8, 2026
