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
Favorite We’re announcing new capabilities in Managed Agents in Gemini API so developers can build reliable, production-ready agents. View Original Source (blog.google/technology/ai/) Here.
Favorite Sharing data internally across teams, externally with partners, or using it for workloads such as machine learning (ML) model training is fundamental to modern business operations. However, when that data contains Personally Identifiable Information (PII), organizations face significant legal and compliance obligations under regulations such as the General Data
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Shared by AWS Machine Learning July 7, 2026
Favorite When you build enterprise agents that execute multi-step workflows, you face a fundamental training challenge. These agents query databases, call APIs, cross-reference results, and recover from mid-process failures. The quality of any single action depends on what happens several steps later. Standard reinforcement learning from human feedback (RLHF) optimizes
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Shared by AWS Machine Learning July 7, 2026
Favorite Organizations are increasingly adopting open-weight foundation models (FMs) to power production AI workloads, from agentic coding assistants to long-context document analysis. As these workloads move from experimentation to enterprise deployment, two requirements shape every model selection decision: the model must deliver the capabilities the workload demands, and the inference
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Shared by AWS Machine Learning July 7, 2026
Favorite Organizations deploying foundation models (FMs) often encounter a common challenge: model safeguards designed for content moderation can also prevent legitimate, business-critical use cases. A media company summarizing scripts with mature language, a cyber security firm simulating real-world threats, or a legal team processing sensitive evidence may all find that
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Shared by AWS Machine Learning July 7, 2026
Shared by AWS Machine Learning July 8, 2026
