Módulo 1: Why and When do we need MLOps
- Discuss Data Scientists’ pain points
- Identify ML Engineering characteristics and challenges
- Define how Google Cloud can help with MLOps
- Recognize how MLOps differs from manual ML management
- Compare and contrast DevOps vs MLOps
Módulo 2: Understanding the Main Kubernetes Components (Optional)
- Define what is a Docker container
- Create Docker containers
- Identify the architecture of Kubernetes: pods, namespaces
- Create Docker containers using Google Container Builder
- Store container images in Google Container Registry
- Create a Kubernetes Engine cluster
- Manage Kubernetes deployments
Módulo 3: Introduction to AI Platform Pipelines
- Identify the benefits and opportunities of AI Pipelines
- Define Access Controls within AI Pipelines
- Recognize pipeline components
- List pipeline workflows
- Set up AI Platform Pipelines
- Create a machine learning pipeline
- Run a machine learning pipeline
- Connect to AI Platform Pipelines using the Kubeflow Pipelines SDK
- Configure a Google Kubernetes Engine cluster for AI Platform Pipelines
Módulo 4: Training, Tuning and Serving on AI Platform
- Identify the main concepts of MLOps on AI Platform
- Create a reproducible dataset
- Implement a tunable model
- Build and push a training container
- Train and tune a model
- Serve and query a model
Módulo 5: Kubeflow Pipelines on AI Platform
- Recognize how Kubeflow Pipelines fits in MLOps
- Describe a Kubeflow Pipeline with KF DSL
- Use the various Kubeflow components
- Compile, upload, and run a pipeline build in Kubeflow Pipelines
Módulo 6: CI/CD for Kubeflow Pipelines on AI Platform
- Create Cloud Build Builders
- Configure pipelines with Cloud Build
- Create triggers for training models using Cloud Build Triggers
- Adopt the best CI/CD practices in the context of ML systems
Módulo 7: Summary