A Sensible Information to Constructing, Automating, and Scaling Machine Studying Pipelines with Fashionable Instruments and Finest Practices
What you’ll study
Perceive the core ideas, advantages, and evolution of MLOps.
Be taught the variations between MLOps and DevOps practices.
Arrange a version-controlled MLOps undertaking utilizing Git and Docker.
Construct end-to-end ML pipelines from knowledge preprocessing to deployment.
Transition ML fashions from experimentation to manufacturing environments.
Deploy and monitor ML fashions for efficiency and knowledge drift.
Acquire hands-on expertise with Docker for ML mannequin containerization.
Be taught Kubernetes fundamentals and orchestrate ML workloads successfully.
Arrange native and cloud-based MLOps infrastructure (AWS, GCP, Azure).Troubleshoot frequent challenges in scalability, reproducibility, and reliability.
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