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.
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…
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.
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…
Our machine learning scientists have been researching ways to enable the semantic search of code.
Build what’s next on GitHub, the place for anyone from anywhere to build anything.
