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#Tutorials and practice

All collected and published items, ordered by time. Picks are a selected subset.
First-party

Direct announcements from model vendors, companies and research institutions on their official sites, blogs and accounts.

Today10/17Sat
  1. AWS Machine Learning Blog60

    Building trust in AI agents: A production-ready evaluation framework with Amazon Bedrock AgentCore

    AWS explains how its SMGS Operations team built a nine-stage evaluation framework with Amazon Bedrock AgentCore for an internal sales intelligence system. The framework combines domain-specific evaluators, versioned golden datasets, CI/CD gates, continuous monitoring, and trace-driven improvements across tools, agents, and multi-agent workflows. AWS reports that automated evaluation reduced quality regression detection from days or weeks to under four hours.

    Why it matters: The framework offers a transferable way to connect domain-specific failure categories with release gates and production monitoring across tools, individual agents, and multi-agent workflows.

10/16Fri
  1. AWS Machine Learning Blog73

    Introducing Reflection Beam, an open-weight model for agentic coding on AWS

    Reflection AI introduced Beam, an open-weight model for agentic coding and multi-step tasks, with 501 billion total parameters and 23 billion active parameters. Reflection reports an effective context length of 1 million tokens and releases Beam 1.0 under Apache 2.0 in BF16, FP8, and NVFP4 variants. The guide explains deployment through Amazon SageMaker JumpStart or HyperPod Inference with JumpStart, SGLang, or vLLM, including capacity planning, invocation, performance measurement, and cleanup.

    Why it matters: The deployment comparison clarifies when managed endpoints suffice and when Kubernetes infrastructure or custom runtimes offer useful control over serving configuration, distributed execution, and scaling.

  2. AWS Machine Learning Blog60

    Meta’s Muse Spark Open now available in Amazon SageMaker JumpStart

    Meta’s coding-optimized open-weight model Muse Spark Open is now available in Amazon SageMaker JumpStart in BF16 and NVFP4 variants, with a 1 million-token context window and 816B total parameters, including 42B active per token. AWS provides deployment instructions using SageMaker Studio or the SageMaker Python SDK, plus inference and cleanup examples. At launch, availability covers US East (N. Virginia) and US East (Ohio).

    Why it matters: The deployment walkthrough connects BF16 and NVFP4 memory requirements to supported instance types, giving teams a concrete basis for planning infrastructure when evaluating the model on their repositories.

  3. Google DeepMind49

    Google Antigravity Walkthrough Finds an Antimicrobial Peptide Candidate

    Google DeepMind describes how biologists used Google Antigravity to find a novel antimicrobial peptide candidate in *Streptococcus pseudopneumoniae*. ProtNLM helps scan millions of previously uncharacterized UniProt proteins, while the agent coordinates database searches, structural analysis, and literature synthesis. The candidate has predicted activity against pathogens involved in respiratory tract infections and is undergoing wet-lab validation.

10/15Thu
  1. AWS Machine Learning Blog64

    AWS explains Trust in Depth for giving agents autonomy within enforceable boundaries

    AWS’s Shawn Bice explains Trust in Depth, a framework for governing agents through four layers: Scope, Know, Bound, and Observe. He describes how AWS tools support policies, agent identities, external enforcement, and tracing, using employee offboarding to illustrate sequence-dependent safeguards. Automated reasoning can prove precisely stated rules within a system’s defined assumptions, while context-dependent expectations require evaluation and human judgment.

    Why it matters: The offboarding example offers a transferable method for translating business requirements into enforced prerequisites, showing how action order can matter as much as individual tool permissions.

  2. AWS Machine Learning Blog58

    Build self-improving document processing using Amazon Bedrock AgentCore

    AWS explains how to build a document processing system using Amazon Bedrock AgentCore and eight specialized agents that classify documents, extract data, validate results, and save refined extraction instructions for reuse. In its purchase order example, processing takes 3 minutes 49 seconds for an unfamiliar format and 1 minute 30 seconds for a subsequent document using stored instructions. The guide includes deployment steps and explains how to customize schemas, validation logic, and prompts.

10/14Wed
  1. AWS Machine Learning Blog45

    Deploy and Evaluate Models with Amazon SageMaker JumpStart

    Amazon SageMaker JumpStart provides a workflow to deploy a pretrained model, run a repeatable performance test with the SageMaker Python SDK, and delete the endpoint afterward. The test measures latency, estimated output length, and single-request generation rate using sample prompts. Model Hub discovery and preconfigured endpoint options simplify browsing and deployment, while a deep link integration lets users deploy from Hugging Face model discovery in a single click.

  2. AWS Machine Learning Blog44

    How Tamnoon Built Tami for Cloud Security Remediation on Amazon Bedrock

    Tamnoon built Tami, an agentic cloud remediation orchestrator on Amazon Bedrock that investigates cloud security findings, assesses whether fixes are safe, and orchestrates remediation with guardrails and governance. The company reports an over 90 percent reduction in critical cloud exposure in 90 days, with zero production issues.

  3. AWS Machine Learning Blog47

    Generate presentations with Amazon Bedrock AgentCore

    AWS’s post explains how to build an AI agent system with Amazon Bedrock AgentCore to create client PowerPoint presentations from enterprise documents. In internal testing, the workflow completed in under 90 seconds; it uses a supervisor agent, RAG, and specialized tools for presentation generation and summarization. The post includes a public repository with code assets.

  4. GitHub Blog · AI & ML68

    GitHub’s model for secret detection: Finding more secrets with less noise

    GitHub explains how its secret scanning pipeline combines regex candidate detection with a fine-tuned ModernBERT classifier to distinguish real credentials from lookalikes using surrounding context. Compared with the previous LLM pipeline, offline evaluation showed 13% higher precision and 19% higher recall; a customer A/B experiment showed 11.5% higher repository owner-weighted acceptance. Median post-push scan execution time fell 86%, and serving costs dropped more than 98%, making it practical to extend generic secret detection to push protection.

    Why it matters: GitHub’s pipeline illustrates a transferable design pattern for narrow classification tasks, combining deterministic candidate generation with a compact contextual model trained on realistic hard negatives.

  5. AWS Machine Learning Blog50

    Manage private pricing offers for Amazon Bedrock with managed entitlements

    AWS explains two ways to activate managed entitlements for Amazon Bedrock private pricing offers across AWS accounts. A lightweight script supports one-time activation of trusted received licenses, while AWS CDK automation processes agreement events, verifies sellers against an allow-list, and creates and activates distributed grants. Negotiated pricing applies only after grants reach ACTIVE or WORKFLOW_COMPLETED; the guide also covers checking grant status and validating charges in AWS Cost Explorer after billing data appears.

  6. AWS Machine Learning Blog63

    Natural language security queries at Orca Security using Amazon Bedrock

    Orca Security partnered with AWS to build Discovery AI on Amazon Bedrock, translating natural language questions into validated Sonar queries across 900+ cloud infrastructure and application models. The architecture combines example retrieval and reranking, relevant schema selection, agentic generation, and validation, with domain experts continuously updating the example bank. Compared with its initial baseline, Orca reports 90 percent lower costs, 93 percent fewer input tokens, and 75 percent lower latency, with response times falling from approximately 60 seconds to 15 seconds.

    Why it matters: The architecture offers a transferable approach to querying large schemas by retrieving validated examples, selecting relevant schema fragments, and feeding validation errors back into generation.

10/13Tue
  1. AWS Machine Learning Blog63

    Mixture-of-Experts kernel engineering on Amazon SageMaker AI

    AWS explains three fused MoE optimizations used in Amazon SageMaker AI, reporting up to 10 percent higher throughput and up to 9 percent faster Time to First Token (TTFT). On H200, swapping matrix operands improved throughput by 2–4 percent, while routing FP8 tensor-quantized models to Triton improved throughput by 10 percent. Eliminating unnecessary Triton JIT recompilation improved TTFT by 9 percent at 8-way tensor parallelism; the post identifies availability in vLLM and the AWS LMI v28 container.

    Why it matters: The optimization examples offer a transferable approach to MoE serving performance by examining matrix layout, backend selection, and compiler specialization as separate sources of throughput and latency overhead.

  2. AWS Machine Learning Blog75

    Build long-horizon agents with Grok 4.7 on Amazon Bedrock

    AWS demonstrates how to build and evaluate a tool-calling agent using Grok 4.7 on Amazon Bedrock, comparing four reasoning effort levels across three synthetic tasks. The agent answered 67 of 72 runs correctly; xhigh achieved 18 of 18, while low achieved 15 of 18 using about an eighth of the output tokens. The guide covers task design, automated grading, production monitoring, and operational controls, noting that these small-scale results are indicative rather than statistically conclusive.

    Why it matters: Tasks with provably unique answers and repeated runs provide a reusable way to compare reasoning effort, while separate correctness and token measurements make the trade-off concrete.

  3. AWS Machine Learning Blog56

    How ShipStation Global built an AI IT help desk on Amazon Bedrock AgentCore

    ShipStation Global built Deckhand, a Slack IT help desk agent on Amazon Bedrock AgentCore, to answer questions, analyze error screenshots, and create or update Jira Service Management tickets. The implementation guide explains its Strands agent, knowledge retrieval, MCP tools, and extraction of resolved Slack threads using DSPy’s Recursive Language Model. In its first three months, 812 of 1,221 agent conversations were handled in chat, while 409 became tickets; positive feedback rose from 82 percent in April to 96 percent in June.

  4. AWS Machine Learning Blog67

    Guide to accessing OpenAI GPT-6 models on Amazon Bedrock with global cross-Region inference

    AWS explains how to invoke OpenAI GPT-6 Sol and GPT-6 Luna through Amazon Bedrock global cross-Region inference from Thailand, Malaysia, Singapore, Indonesia, and Taiwan, and GPT-6 Astra from Singapore. The guide covers Converse, OpenAI-compatible APIs, coding clients, IAM permissions, SCP exceptions, quotas, and monitoring. Billing and quota tracking remain in the source Region, while requests run in supported destination Regions; each output token consumes 10 tokens of quota.

    Why it matters: The guide provides a transferable approach to configuring cross-Region inference permissions by scoping SCP exceptions to inference profiles while preserving Region controls for other services.

  5. GitHub Blog · AI & ML49

    How to Teach GitHub Copilot About Your Codebase

    GitHub’s beginner guide explains four ways to tailor Copilot to a project: custom instructions for repository conventions, agent skills for repeatable procedures, custom agents for broader specialized tasks, and MCP servers for connecting to external tools and data. It recommends starting with a convention you often explain and adding it to `.github/copilot-instructions.md`.

  6. AWS Machine Learning Blog66

    Run multi-node inference workloads using Ray LLM Deep Learning Containers

    AWS shows how to deploy Qwen3.5-9B across two GPU nodes on Amazon EKS using Ray LLM Deep Learning Containers, with separate prefill and decode deployments behind one OpenAI-compatible endpoint. The walkthrough configures the application in YAML and uses NIXL over EFA to transfer the KV cache between nodes. It covers cluster provisioning, model invocation, transport verification, and resource cleanup.

    Why it matters: The guide provides a transferable deployment pattern for separating prefill and decode, including how to verify the transport backend and avoid a silent fallback to TCP-speed communication.

  7. AWS Machine Learning Blog61

    Deploy decision models with llama.cpp on Amazon SageMaker AI

    AWS explains how to deploy GGUF models with its llama.cpp Deep Learning Container on Amazon SageMaker AI, then add a SemIf adapter that returns probabilities over declared options without generating answer tokens. The guide includes CPU and GPU deployment code. Across 231 JevBench tasks using Qwen3.5-4B Q4_K_M, the GPU configuration delivered 4.9 times faster p50 and 32.2 times faster p95 latency than the CPU configuration, with similar accuracy.

    Why it matters: The deployment pattern shows how applications can score predefined choices without parsing generated prose, while keeping authorization, thresholds, and fallback rules under application control.

10/9Fri
  1. Hugging Face Blog34

    How Ai2 Rebuilt Cluster Scheduling with GPU Time Budgets

    Ai2 replaced its original priority scheduler with GPU time budgets, hierarchical fair allocation, and time-slice contracts, turning debates over how much GPU time each research project should get from case-by-case operational decisions into a transparent budget allocation process. Its cluster manages thousands of NVIDIA H100, B200, and B300 GPUs, with scales ranging from 88 to 1,024 GPUs, and demand from roughly 150 internal researchers has long been 2-3 times the available compute.

  3. Hugging Face Blog73

    Hugging Face author trains six models in two days with ML-intern, spending about $103 in total

    The author used ML-intern in HuggingChat, describing the requirements in prompts, to build six models in two days and publish them to the Hub, at a total compute cost of about $103. One project distilled the 9B prompt rewriter for Qwen-Image 2.1 into a 0.8B version that runs on a CPU, with a 99.7% valid-output rate and about one-quarter of the teacher model's token usage. The author published all seven prompts and recommends asking for baseline comparisons, smoke tests, and a budget cap.

    Why it matters: The author built six models in two days with ML-intern and published all prompts and cost details, offering an approach others can adapt to their own training workflows.

  4. NVIDIA Blog59

    Into the Omniverse: How Developers Turn Ideas Into Simulations With Frontier AI Agents

    NVIDIA explains how its developers use frontier AI agents, including GPT-6 Astra, with Omniverse libraries to build and refine simulations through natural-language instructions, human review and iterative testing. Examples span warehouse scaling, cable routing, robot asset preparation, autonomous-driving workflows and sensor-based digital twin refinement. One warehouse simulation sustained 30 frames per second with 80 conveyors, 40 arms and 800 boxes during a two-minute test; a robot CAD example used estimated physical properties that were not calibrated against the real robot.

10/8Thu
10/7Wed
  1. AWS Machine Learning Blog36

    How Cornerstone OnDemand cut database diagnosis time by 78% with Amazon Bedrock

    Cornerstone OnDemand built Orion AI, a multi-agent system based on Amazon Bedrock and the open-source agent orchestration framework Strands Agents. It reduced database diagnosis time from 45 minutes to 10 minutes, a 78% reduction. It also compressed manual lifecycle operations from more than 10 steps to one interaction, a 70% reduction, and cut median redundant alerts by 65%. A three-person team delivered it in six months.

  5. AWS Machine Learning Blog60

    How to build a context-aware AI assistant on AgentCore and OpenClaw

    An AWS blog post explains how to run the open-source agent system OpenClaw on the Amazon Bedrock AgentCore runtime to build Sprout, a personal assistant with long-term memory.

    Why it matters: The source provides reusable methods for container wrapping, namespaces, and prompt-cache ordering, helping readers build an agent with long-term memory.

  7. AWS Machine Learning Blog22

    Management and governance best practices for Amazon SageMaker HyperPod

    Amazon SageMaker HyperPod can connect clusters to projects through SageMaker Unified Studio, letting team members launch training and fine-tuning workloads in workspaces while retaining governance controls at the infrastructure level. The article proposes a four-layer control model—organization, project, cluster, and workload—and explains how to design identity, capacity, and observability policies across those layers. Infrastructure teams retain control of cluster operations while ML teams use approved compute in project contexts.

10/6Tue
10/3Sat
  4. AWS Machine Learning Blog38

    Use Amazon Quick and the Adjudicated Query pattern to check thousands of leases for compliance

    AWS proposed the Adjudicated Query design pattern, in which business users ask compliance questions in the Amazon Quick chat interface while a deterministic rules engine makes the actual decision. An integrity receipt ensures that the sum of compliant, noncompliant, ambiguous, and unreadable records equals the total scanned, preventing records from being silently skipped. The pattern applies to high-risk cases such as lease compliance, sanctions screening, and insurance claims.

10/2Fri
  1. GitHub Blog · AI & ML22

    AI is changing how developers work; GitHub highlights three skills to strengthen

    GitHub says developers need to strengthen three skills in the AI era: learn to direct AI agents rather than merely use them, do not trust an AI's first answer, and use time saved with AI to tackle bigger problems. It points to GitHub Copilot's built-in Rubber Duck agent, which uses a second model to critically review plans, code, and tests and catch issues the first model missed.