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Table of Contents
Table of Contents
Machine learning models that deliver business value require more than just a good algorithm; they need robust operational practices to ensure reliability, scalability, and continuous improvement.
What You Will Learn
- 1 Learn the foundations and lifecycle of MLOps to align ML initiatives with business goals.
- 2 Understand data management and feature engineering for building reliable ML pipelines.
- 3 Apply experiment tracking and reproducibility techniques for systematic model development.
- 4 Create CI/CD pipelines tailored for ML systems to automate testing and deployment.
- 5 Implement model deployment and serving strategies for low-latency production inference.
- 6 Analyze monitoring and observability to detect drift and maintain model performance.
- 7 Explore governance, compliance, and security in ML systems for responsible AI.
- 8 Develop infrastructure automation using infrastructure as code for ML environments.
- 9 Build a culture of continuous learning and improvement for MLOps teams.
- 10 Design end-to-end MLOps platforms incorporating feedback loops and retraining.
What's Included
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MLOps Engineering