MLOps for Engineers: From Jupyter Notebook to Production AI Deployment
Learn MLOps practices, model versioning, CI/CD for ML, containerization, monitoring, and deploying AI models in industrial environments.
Building an ML model is only 20% of the work. The other 80% is getting it into production, keeping it running, and monitoring its performance. That is where MLOps comes in.
MLOps Pipeline
- -Data versioning (DVC)
- -Experiment tracking (MLflow)
- -Model training and validation
- -Containerization (Docker)
- -API serving (FastAPI, TF Serving)
- -Monitoring and alerting
- -Retraining triggers
EDWartens Digital AI advanced programs cover the complete MLOps lifecycle.
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