This article provides a comprehensive guide to setting up a complete MLOps system on a single laptop. It covers the entire workflow from API request validation to versioned predictions, detailing the use of tools like FastAPI, Celery, MLflow, Kubernetes, and Terraform. The guide also includes instructions for implementing monitoring within the MLOps pipeline. AI
IMPACT Provides a practical guide for engineers to implement end-to-end MLOps workflows on local hardware.
RANK_REASON The article describes a technical guide for setting up MLOps infrastructure and tools, which falls under the 'tool' category.
- Celery
- Docker
- FastAPI
- Git
- GitHub
- Jupyter Notebook
- Kubernetes
- MLflow
- MLOps
- PyTorch
- scikit-learn
- TensorFlow
- Terraform
- Visual Studio Code
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