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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 .
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.
How to build the “Trust Layer” for GitHub Copilot cloud agent without brittle scripts or black-box judgements by using dominatory analysis.
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.
Discover GitHub Agentic Workflows, now in technical preview. Build automations using coding agents in GitHub Actions to handle triage, documentation, code quality, and more.
Discover tips, technical guides, and best practices in our biweekly newsletter just for devs.
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.
Learn how we’re experimenting with open source AI models to systematically incorporate customer feedback to supercharge our product roadmaps.
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…
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…
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.
Our machine learning scientists have been researching ways to enable the semantic search of code.
