Favorite Public sector agencies process large volumes of unstructured evidence, such as body camera footage, surveillance video, and scanned documents, that require extracting insights before anyone can act on them. This post shows how to combine Amazon Bedrock Data Automation with the Model Context Protocol (MCP) to turn unstructured data
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Shared by AWS Machine Learning September 22, 2026
Favorite Detecting industrial safety risks in seconds, not minutes, is what keeps workers safe on an active plant floor. This is what Tata Elxsi set out to deliver by building IRIS, a real-time industrial safety platform on AWS. In this post, we show how Tata Elxsi built IRIS (Industrial Real-Time
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Shared by AWS Machine Learning September 22, 2026
Favorite Trane Technologies manages millions of connected heating, ventilation, and air conditioning (HVAC) assets worldwide, but getting a single operational answer could mean cross-referencing multiple dashboards and drilling through menus for 20 minutes or more. For organizations operating at this scale, that kind of friction slows operations, defers corrective action,
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Shared by AWS Machine Learning September 22, 2026
Favorite Concurrency sweeps help you right-size a generative AI endpoint by finding the instance type and serving configuration that maximizes price-performance while holding latency within acceptable bounds. Without a systematic approach, right-sizing means deploying, load-testing manually, adjusting, and repeating until the numbers look acceptable. Choose five ml.g7e.2xlarge instances when one
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Shared by AWS Machine Learning September 22, 2026
Favorite Reactiv offers a mobile commerce product that helps Shopify merchants launch and manage native mobile apps, where shoppers convert at 2–4 times the rate of web visitors. For these merchants, a stale homepage or a missed promotional window costs real revenue. Yet keeping an app fresh requires constant manual
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Shared by AWS Machine Learning September 22, 2026
Favorite Deploying a Hugging Face model to production means making a dozen decisions: choosing the right serving container for the model’s architecture, confirming the current image tag for your AWS Region, and matching an instance type to the model’s memory footprint. Beyond infrastructure, you must wire autoscaling so you don’t
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Shared by AWS Machine Learning September 19, 2026
Favorite Agents are no longer experiments. They process claims, write and review code, coordinate across systems, and run for hours without supervision. As agents take on more complex, longer-running work, the infrastructure underneath them must evolve just as fast. We built Amazon Bedrock AgentCore to help developers build, connect, and
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Shared by AWS Machine Learning September 19, 2026
Favorite Organizations building multi-model agentic AI applications face growing infrastructure complexity. Managing container orchestration, scaling policies, identity, and observability for multiple model types adds operational overhead. Teams often spend more time on infrastructure than on agent logic development. Developers running agentic frameworks on self-managed infrastructure such as Amazon Elastic Container
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Shared by AWS Machine Learning September 19, 2026
Favorite Open-weight models are changing the economics of building and deploying AI at scale. Rapid gains in intelligence and efficiency mean companies can match each workload with the right balance of capability, speed, and cost. AWS is building for a future in which organizations can adopt open-weight innovation with the
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Shared by AWS Machine Learning September 19, 2026
Favorite Generative AI inference is uniquely hard: models are tens to hundreds of gigabytes, latency requirements are measured in tokens per second, cold starts can span multiple minutes as containers and weights transfer, GPU capacity is constrained, and traditional monitoring tools expose none of the token-level signals that matter in
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Shared by AWS Machine Learning September 19, 2026