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MLOps Zoomcamp hosted by DataTalksClub.
Jupyter Notebook 9.73%
Python 0.48%
Dockerfile 0.01%
HTML 89.77%
Shell 0.01%
Makefile 0.01%
mlops-camp's Introduction
Starting this MLOps Zoomcamp
- 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
- Homework
- 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
More details
- CRISP-DM, CRISP-ML
- ML Canvas
- Data Landscape canvas
- MLOps Stack Canvas
- Documentation practices in ML projects (Model Cards Toolkit)
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