https://github.com/froukje/ml-ops-zoomcamp
notes and excercises for the mlops-zoomcamp from DataTalksClub
https://github.com/froukje/ml-ops-zoomcamp
mlops zoomcamp
Last synced: 3 months ago
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notes and excercises for the mlops-zoomcamp from DataTalksClub
- Host: GitHub
- URL: https://github.com/froukje/ml-ops-zoomcamp
- Owner: froukje
- Created: 2022-05-19T06:22:11.000Z (about 3 years ago)
- Default Branch: master
- Last Pushed: 2022-09-02T07:46:46.000Z (over 2 years ago)
- Last Synced: 2025-01-13T20:28:46.572Z (5 months ago)
- Topics: mlops, zoomcamp
- Language: HTML
- Homepage:
- Size: 15.3 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
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README
The content of this repsitory is the result of following the ml-ops-zoomcamp given by [Data Talks Club](https://github.com/DataTalksClub/mlops-zoomcamp)
## Module 1: Introduction
* What is MLOps
* MLOps maturity model
* Running example: NY Taxi trips dataset
* Why do we need MLOps
* Course overview
* Environment preparation
* Homework
## Module 2: Experiment tracking and model management
* Experiment tracking intro
* Getting started with MLflow
* Experiment tracking with MLflow
* Saving and loading models with MLflow
* Model registry
* MLflow in practice
* Homework
## Module 3: Orchestration and ML Pipelines
* Workflow orchestration
* Prefect 2.0
* Turning a notebook into a pipeline
* Deployment of Prefect flow
* Homework
## Module 4: Model Deployment
* Batch vs online
* For online: web services vs streaming
* Serving models in Batch mode
* Web services
* Streaming (Kinesis/SQS + AWS Lambda)
* Homework
## Module 5: Model Monitoring
* ML monitoring vs software monitoring
* Data quality monitoring
* Data drift / concept drift
* Batch vs real-time monitoring
* Tools: Evidently, Prometheus and Grafana
* Homework
## Module 6: Best Practices
* Devops
* Virtual environments and Docker
* Python: logging, linting
* Testing: unit, integration, regression
* CI/CD (github actions)
* Infrastructure as code (terraform, cloudformation)
* Cookiecutter
* Makefiles
* Homework
## Module 7: Processes
* CRISP-DM, CRISP-ML
* ML Canvas
* Data Landscape canvas
* MLOps Stack Canvas
* Documentation practices in ML projects (Model Cards Toolkit)
## Project
* End-to-end project with all the things above