Machine learning for business decision optimization

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.

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.

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.

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