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MLOps Certification- Pipeline basics to MLOps Toolbox


This course introduces members to MLOps ideas and finest practices for deploying, evaluating, monitoring and working manufacturing ML methods on each cloud and Edge. MLOps is a self-discipline targeted on the deployment, testing, monitoring, and automation of ML methods in manufacturing. Machine Studying Engineering professionals use instruments for steady enchancment and analysis of deployed fashions. They work with Knowledge Scientists, who develop fashions, to allow velocity and rigor in deploying the most effective performing fashions.

This course encompasses the next matters;

1. Introduction of Knowledge, Machine Studying Mannequin and Code with reference to MLOps.

2. MLOps vs DevOps.

3. The place and How to Deploy MLOps.

4. Parts of MLOps.

5. Steady X & Versioning in MLOps.

6. Experiment Monitoring in MLOps.

7. Three Ranges of MLOps.

8. How to Implement MLOps?

9. CRISP (Q)- ML Life Cycle Course of.

10. Full MLOps Toolbox.

11. Google Cloud architectures for dependable and efficient MLOps environments.

12. Working with AWS MLOps Providers.

By the tip of this course, you’ll be prepared to:

  1. Design an ML manufacturing system end-to-end: information wants, modeling methods, and deployment necessities.

  2. How to develop a prototype, deploy, and repeatedly enhance a production-sized ML software.

  3. Perceive information pipelines by gathering, cleansing, and validating datasets.

  4. Set up information lifecycle by leveraging information lineage.

  5. Use analytics to handle mannequin equity and mitigate bottlenecks.

  6. Ship deployment pipelines for mannequin serving that require completely different infrastructures.

  7. Apply finest practices and progressive supply methods to keep a repeatedly working manufacturing system.



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