A Practical Guide to Building, Automating, and Scaling Machine Learning Pipelines with Modern Tools and Best Practices
What you will learn
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Understand the core concepts, benefits, and evolution of MLOps.
Learn the differences between MLOps and DevOps practices.
Set up a version-controlled MLOps project using Git and Docker.
Build end-to-end ML pipelines from data preprocessing to deployment.
Transition ML models from experimentation to production environments.
Deploy and monitor ML models for performance and data drift.
Gain hands-on experience with Docker for ML model containerization.
Learn Kubernetes basics and orchestrate ML workloads effectively.
Set up local and cloud-based MLOps infrastructure (AWS, GCP, Azure).Troubleshoot common challenges in scalability, reproducibility, and reliability.
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