This course provides a foundational understanding of MLOps principles and practices. Learn to build reliable and efficient machine learning systems by applying operational rigor to the entire ML lifecycle, from data management and model training to deployment and monitoring.
Prerequisites Basic Python & ML concepts
Level:
MLOps Fundamentals
Explain the purpose of MLOps and its core principles for production machine learning.
ML Lifecycle Management
Describe the stages of the machine learning lifecycle and how MLOps streamlines them.
Automation and CI/CD
Understand the role of automation and CI/CD pipelines in machine learning.
Versioning Techniques
Implement basic version control for data, code, and models to ensure reproducibility.
Model Deployment and Monitoring
Grasp the basics of deploying and monitoring machine learning models in a production environment.
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