Servindo modelos de ML em produção: FastAPI + Docker + AWS Lambda
📰 Medium · Python
Learn to serve ML models in production using FastAPI, Docker, and AWS Lambda for scalable and efficient deployment
Action Steps
- Build a RESTful API using FastAPI to serve ML models
- Containerize the API using Docker for easy deployment
- Configure AWS Lambda to handle API requests and scale automatically
- Test the deployment using sample data and verify model performance
- Deploy the model to a production environment and monitor its performance
Who Needs to Know This
Data scientists and machine learning engineers can benefit from this tutorial to deploy their models in a production-ready environment, while DevOps teams can use this to streamline their model serving pipeline
Key Insight
💡 Using FastAPI, Docker, and AWS Lambda allows for scalable and efficient deployment of ML models in production
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Serve ML models in production with FastAPI, Docker, and AWS Lambda!
Key Takeaways
Learn to serve ML models in production using FastAPI, Docker, and AWS Lambda for scalable and efficient deployment
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> Parte 5 da série IA na Prática — Do Modelo à Produção Continue reading on Medium »
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