News For Dev-ops

Amazon SageMaker Unified Studio gives project members full Git version control directly within the tools you already use - Query Editor, Visual ETL, Workflows, and Notebooks. The enhanced Repositories experience replaces the previous automatic sync approach with flexible, file-level version control. This brings a consistent source control experience across all project tools, including Notebooks, which previously had no Git support. You choose exactly which files to track in Git by adding them to a repository on GitHub, GitLab, or Bitbucket - source control is not enforced at the project level, so you decide what gets versioned and when. When you're ready, you commit and push all your changes in a single action. Repositories are decoupled from project creation, meaning you can add a repository to a project at any point after the project is created, as your needs evolve. Projects can connect to any number of repositories and branches at the same time, and you can create branches, ...

Windows Performance Analyzer (WPA) is one of the most powerful tools available for understanding system performance on Windows. It helps engineers investigate Event Tracing for Windows (ETW) traces across CPU, memory, disk, networking, scheduling, input, and many other areas of the operating system. That power also comes with complexity. A single trace can contain a huge amount of data, and finding the right signal often requires knowing which WPA tables to open, which columns matter, how to filter the right time range, and how to connect multiple pieces of evidence into a root cause. We are working on WPA MCP as an Early Preview feature to make that workflow easier and more approachable. What is WPA MCP? WPA MCP brings GitHub Copilot CLI into the WPA trace-analysis workflow. Instead of starting every investigation by manually navigating graphs and tables, you can ask questions about the trace in natural language. WPA MCP helps GiHub Copilot translate that intent into trace-data exp...

Duplicate patient records are one of the costliest problems in healthcare data management, scattering a patient's information, leading to redundant tests, missed diagnoses, billing errors, manual reconciliation, and broken analytics that count one patient as many. AWS HealthLake now supports resource matching, which automatically identifies and links duplicate records in a datastore. Healthcare organizations can build accurate longitudinal patient records and trustworthy population health datasets without specialized master data management tooling. Resource matching works across seven Fast Healthcare Interoperability Resources (FHIR) resource types: Patient, Practitioner, Organization, Location, Device, RelatedPerson, and PractitionerRole. It matches high-confidence healthcare identifiers such as Social Security, medical record, and national provider numbers, applying each identifier's real-world scope and filtering out placeholder values to avoid false matches. Once enab...

Today, AWS announces the general availability of node lifecycle actions in AWS Parallel Computing Service (PCS). With node lifecycle actions, you can run custom scripts automatically at defined points in a compute node's lifecycle. You can use them to prepare your nodes for work. For example, you can mount shared storage, join a directory service, install software, or set up monitoring. You define node lifecycle actions in your PCS compute node group configuration when you create or update the group, and you can reuse the same script across multiple compute node groups and clusters. For each script, you set its location as an Amazon S3 or HTTPS URI, the arguments to pass, which lifecycle stage it runs in, whether it re-runs on reboot, and its error-handling behavior. AWS PCS writes the output to a dedicated log file, giving you visibility into what ran. AWS PCS is a managed service that simplifies running and scaling high performance computing (HPC) workloads on AWS using Slurm...

Amazon Connect Customer now automatically surfaces relevant examples of an agent's evaluations when managers are preparing coaching feedback, so they can deliver actionable, evidence-backed coaching to agents. When a manager prepares agent coaching feedback in Amazon Connect Customer, they automatically receive examples of recent evaluations where the agent scored high or low on the chosen coaching topic, along with human or AI evaluator notes explaining the agent behaviors that drove the result. For example, while preparing feedback for an agent on "de-escalation," a manager receives example calls where the agent successfully calmed a frustrated customer, alongside calls that were escalated, with insights into the agent behaviors that led to each outcome. This enables managers to deliver tailored coaching that accelerates agent performance improvement, while saving the time spent manually searching for examples. This feature is available in all regions where Amazon Con...

Not every question deserves the same amount of thought. Renaming a variable isn't the same as debugging a memory leak, and they don't need the same level of thinking. So why should your model treat them the same way every time? Starting in Visual Studio 18.9 Insiders 2, it doesn't have to. Supported models now come with a thinking effort control, so you can dial up the reasoning when a problem is genuinely hard and dial it back down when it isn't. It's a small knob with a big payoff: better-matched answers, and more control over how many credits you spend getting them. A dial, not a switch Thinking effort is exactly what it sounds like: how much reasoning a model does before it answers. It shows up as a set of named levels, and which ones you get depends on the model. Here's what each level is for: Low  - Quick responses with minimal reasoning. Great for straightforward questions and everyday code suggestions, and it consumes fewer AI credits. Mediu...