Automating cross-repo documentation with GitHub Agentic Workflows
Explore how the Aspire team turns merged product changes into SME-reviewed docs pull requests, closing the gap between release and documentation.
Machine learning
Resources and guides for developers focused on building, training, and deploying machine learning (ML) models. Get practical tools and best practices to enhance your work with ML on and off GitHub. You can also experiment with machine learning on GitHub—check out our docs to learn more.
Featured
Improving token efficiency in GitHub Agentic Workflows
Agentic workflows that run on every pull request can quietly accumulate large API bills. Here’s how we instrumented our own production workflows, found the inefficiencies, and built agents to fix them.
Validating agentic behavior when “correct” isn’t deterministic
How to build the “Trust Layer” for GitHub Copilot cloud agent without brittle scripts or black-box judgements by using dominatory analysis.
Under the hood: Security architecture of GitHub Agentic Workflows
GitHub Agentic Workflows are built with isolation, constrained outputs, and comprehensive logging. Learn how our threat model and security architecture help teams run agents safely in GitHub Actions.
Latest
Automate repository tasks with GitHub Agentic Workflows
Discover GitHub Agentic Workflows, now in technical preview. Build automations using coding agents in GitHub Actions to handle triage, documentation, code quality, and more.
How students teamed up to decode 2,000-year-old texts using AI
Students used GitHub Copilot to decode ancient texts buried in Mount Vesuvius, achieving a groundbreaking historical breakthrough. This is their journey, the technology behind it, and the power of collaboration.
How GitHub harnesses AI to transform customer feedback into action
Learn how we’re experimenting with open source AI models to systematically incorporate customer feedback to supercharge our product roadmaps.
How MLOps can drive governance for machine learning: A conversation with Algorithmia
This post features a guest interview with Diego M. Oppenheimer, CEO at Algorithmia Over the past few years, machine learning has grown in adoption within the enterprise. More organizations are…
Using GitHub Actions for MLOps & Data Science
Background Machine Learning Operations (or MLOps) enables Data Scientists to work in a more collaborative fashion, by providing testing, lineage, versioning, and historical information in an automated way. Because the…
C# or Java? TypeScript or JavaScript? Machine learning based classification of programming languages
To make language detection more robust and maintainable in the long run, we developed a machine learning classifier named OctoLingua based on an Artificial Neural Network (ANN) architecture which can handle language predictions in tricky scenarios.
Towards Natural Language Semantic Code Search
Our machine learning scientists have been researching ways to enable the semantic search of code.
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