- What is MLOps
- MLOps maturity model
- Running example: NY Taxi trips dataset
- Why do we need MLOps
- Course overview
- Environment preparation
- Homework
- Experiment Tracking into
- Getting started with MLflow
- Experiment tracking with MLflow
- Model management
- Model registry
- Mlflow in practice
- Homework
- Workflow orchestration
- Prefect 2.0
- Turning a notebook into a pipeline
- Deployment of Prefect Flow
- Homework
- Three ways of model deployment: Online (web and streaming) and offline (batch)
- Web service: model deployment with Frask
- Streaming: consuming events with AWS Kinesis and Lambda
- Batch: scoring data offline
- Homework
- Monitoring ML-based services
- Monitoring web services with Prometheus, Evidently, and Grafana
- Monitoring batch jobs with Prefect, MongoDB, and Evidently
- Testing: unit, integration
- Python: linting and formatting
- Pre-commit hooks and makefiles
- CI/CD (Github Actions)
- Infrastructure as code (Terraform)
- Homework
- End-to-end project with all the things above
- CRISP-DM, CRISP-ML
- ML Canvas
- Data Landscape canvas
- MLOps Stack Canvas
- Documentation practices in ML projects (Model Cards Toolkit)
(In October)
- Larysa Visengeriyeva
- Cristian Martinez
- Kevin Kho
- Theofilos Papapanagiotou
- Alexey Grigorev
- Emeli Dral
- Sejal Vaidya
- Machine Learning Zoomcamp - free 4-month course about ML Engineering
- Data Engineering Zoomcamp - free 9-week course about Data Engineering
| Id | Module Session | Progress | Dead line | Link |
|---|---|---|---|---|
| 01 | Introduction to MLOPS | ⌛ ✔️ | 23/05/2022 | |
| 02 | Exepriment & Tracking | ⌛ ✔️ | 30/05/2022 | |
| 03 | Orchestration | ⌛ ✔️ | 11/06/2022 | |
| 04 | Deployment | ⌛ ✔️ | 27/06/2022 | |
| 05 | Monitoring | ⌛ ✔️ | Optional/07/2022 | |
| 06 | Best Practices | ⌛ ✔️ | 01/08/2022 | |
| 07 | Project first Cohort | ⏳ ✖️ | 22/08/2022 | |
| 07 | Project Second Cohort | ⏳ ✖️ | 05/09/2022 |
Hint
⌛ ✔️= Done || ⏳ ✖️= Not_DONE