MLOps: Reliable monitoring of ML flow, TFX and workflow in Helm and MLOps (Grafana) for CI / CD deployment in ML system
This course introduces participants to the MLOps concept and best practices for deploying, evaluating, monitoring, and operating product ML systems on both the Cloud and Edge. MLOps is a discipline that focuses on the deployment, testing, maintenance, and automation of ML systems in production. Machine learning engineering uses tools for continuous improvement and evaluation of professionally deployed models. They work with data scientists, who develop models, to enable speed and rigidity in deploying the best performing models.
This course covers the following topics;
1. Introduction to data, machine learning models and codes in the context of MLOps.
2. MLOps vs DevOps.
3. Where and how to deploy MLOps.
4. Components of MLOps.
5. Continuous X and versioning in MLOps.
6. Tracking experiments in MLOps.
7. Three levels of MLOps.
8. How to implement MLOps?
9. CRISP (Q) – ML Life Cycle Process.
10. Complete MLOps toolbox.
11. ML Flow Library for MLOps.
12. Tensor Flow Extended (TFX) for deployment of MLOps.
13. PyCaret for evaluation and deployment of MLOps.
14. Kubernets as package manager for MLOps.
11. Google Cloud Architecture for reliable and effective MLOps environment.
12. Working with AWS MLOps services.
Lab exercises with remedies:
1. How to deploy MLOps using helme.
2. Make changes with the helm.
3. Track deployed applications.
4. Share a helmet chart.
At the end of this course, you will be ready to:
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Design an ML production system end-to-end: data needs, modeling strategies and deployment requirements.
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How to develop prototypes, how to deploy and constantly improve product-sized ML applications.
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Understand the data pipeline by collecting, cleaning and standardizing datasets.
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Establish a data lifecycle by taking advantage of the data lineage.
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Use analytics to address model objectivity and reduce barriers.
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Distribute deployment pipelines for model servicing that require a variety of infrastructure.
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Implement best practices and progressive delivery techniques to maintain a continuously working production system.
MLOps: Reliable monitoring of ML flow, TFX and workflow in Helm and MLOps (Grafana) for CI / CD deployment in ML systems
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