Operationalize machine learning at scale — master CI/CD for ML, model monitoring, feature stores, and the full MLOps lifecycle from experimentation to production.
Most ML models never make it to production — MLOps is the discipline that fixes that. This course teaches you to build robust, automated pipelines that take a model from a Jupyter notebook to a scalable, monitored, production service.
You'll work with industry-standard tools: MLflow, Kubeflow, Airflow, DVC, and cloud ML platforms. By graduation you'll be able to design, build, and maintain the full ML lifecycle at any organization.
A battle-tested curriculum covering every stage of the ML production lifecycle.
Data scientists who build great models but struggle to get them deployed and maintained at scale in production.
DevOps and platform engineers looking to specialize in ML infrastructure and build world-class ML platforms.
Software engineers who want to expand into the ML space by mastering the operational and engineering side of AI systems.
MLOps roles command top salaries. Get certified and build the production ML skills every company needs. Next batch starts July 5.
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