To learn about the key components of MLOps, APIs and cloud deployment.
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A glance at ML Life Cycle
- Challenges facing MLOps
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Introduction to MLOps
- What is MLOps?
- Why the need for MLOps?
- Where & when do we adopt MLOps
- Components of MLOps
- Introduction to APIs
- Challenges and the need for APIs in MLOps
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Containers for ML Deployment
- Introduction to Docker
- Introduction to kubernetes
- Deploy machine learning models using docker
- Deployment of containers on kubernetes(EKS, GKE, etc)
- An introduction to automating ML deployment workflow
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Leveraging Cloud Computing for MLOps
- Deploying machine learning model through AWS
- Deploying deep learning model though google cloud
- Train and deploy ML model through Azure Auto ML
- Deploy model via Fastapi, Streamlit, Heroku
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Monitoring and Automation
- Overview of Monitoring
- System infrastructure monitoring
- Data pipeline monitoring
- Monitor and evaluate model performance
- Maintenance guide for model updating
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An introduction to CI/CD for automated model deployment
Learn more about course here.