Practical MLOps with H2O.ai introduces the full machine learning operations (MLOps) lifecycle using the H2O.ai platform.
This course covers how to manage, deploy, and monitor models in real-world settings.
You will see hands-on examples with H2O Driverless AI and H2O Hydrogen Torch, and learn how to use H2O MLOps for governance, versioning, and reliable model performance.
What you'll learn
- MLOps Fundamentals
Understand what MLOps is and why it’s important for managing and scaling models.
- End-to-End Lifecycle
Learn the steps from data preparation and model training to deployment, monitoring, and retraining.
- Model Governance
See how to register models, track versions, and apply governance strategies for compliance and reliability.
- Deploying Driverless AI Models
Practice deploying models from H2O Driverless AI, configuring endpoints, and monitoring them in production.
- Deploying Hydrogen Torch Models
Walk through deploying deep learning and computer vision models into H2O MLOps with proper settings and scoring
- Hands-On Demonstrations
Follow guided demos to configure deployments, monitor model drift, and score data with Python clients.
Course Playlist on YouTube
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