Model CI/CD

Overcome model chaos, automate key workflows, ensure governance, and streamline the end-to-end model lifecycle. This course will provide you with the concepts, best practices, and tools to level up your model management and drive success.

Effective MLOps: Model development

Bringing machine learning models to production is challenging, with a continuous iterative lifecycle that consists of many complex components. Having a disciplined, flexible and collaborative process – an effective MLOps system – is crucial to enabling velocity and rigor, and building an end-to-end machine learning pipeline that continually delivers production-ready ML models and services.

CI/CD for machine learning (GitOps)

Streamline your ML workflows and save valuable time by automating your pipelines and deploying models with confidence. Learn how to use GitHub Actions and integrate W&B experiment tracking in this practical, hands-on learning experience.

Data validation in production ML pipelines

Gain expertise in data validation to build robust production ML pipelines, detect data drift, and manage data quality using cutting-edge automated toolkits.

Machine learning for business decision optimization

Learn to optimize decision rules, translating machine learning predictions into actionable insights. Discover how to achieve practical value and business impact by measuring performance using business metrics, and deploy ML models successfully.

Weights & Biases 201: Registry

This compact course, led by ML Success Engineer Ken Lee, dives into advanced model management utilizing Weights and Biases for logging, registering, and managing ML models.

Weights & Biases 101

This course is a gentle introduction to Weights & Biases with a focus on experiment tracking. Learn to track, visualize, and optimize your ML experiments, streamline collaboration with your team, and make your projects efficient and reproducible.