Course topics
Topic 1: MLops Introduction- ML in real world
- Data Engineering into ML Models
- Roles & Responsibilities
- Data Types and ML Architectures
- Scenarios
- A different version of MLOps
- Typical Tooling
- Model in notebook
- Split training & inference
- Automatic builds
- Containerization
- Training automation
- Model Generation & Repository
- Production deployment
- Integration into existing pipeline
- Model Versioning
- Data set visioning
- Containerization
- Annotation Tooling
- AutoML
- Explainability
- Feature Engineering
- Model Repositories
- Release Pipelines
- AirFlow / Pachyderm
- Code hooks
- KubeFlow / KubeFlow Pipelines
- AWS SageMaker vs Azure ML Studio vs GCP DataLab
- Feature Stores
- Re-training & metainformation
- Data Quality
- Containers/Docker/Kubernetes/KubeFlow
- Inference
- Monitoring
- Model Drift
- Data Drift
- TF Serve/Seldon/KFServe
- Server/Edge/Mobile
- What can go wrong?
- Maturity of the models
- Meta Architecture
- Scenarios – Operations & ML
- Google MLOps Maturity Levels