MLOps Essentials: Model Deployment and Monitoring

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Kumaran Ponnambalam

1:23:31

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01 - Introduction

01 - Getting started with MLOps.mp4

01:20

02 - Course coverage.mp4

03:39

03 - Review of MLOps lifecycle.mp4

03:40

02 - 1. Continuous Delivery

01 - An ML production setup.mp4

02:25

02 - Deployment pipelines.mp4

02:31

03 - Deployment rollout strategies.mp4

04:03

04 - Planning for infrastructure.mp4

03:31

05 - Deployment best practices.mp4

02:06

06 - Tools and technologies for deployment.mp4

00:49

03 - 2. Model Serving

01 - Model serving patterns.mp4

03:00

02 - Scaling model serving.mp4

03:54

03 - Building resiliency in serving.mp4

03:30

04 - Serving multiple models.mp4

03:16

05 - Tools and technologies for serving.mp4

00:57

04 - 3. Continuous Monitoring

01 - The monitoring pipeline.mp4

03:06

02 - Instrumentation for observability.mp4

03:31

03 - Metrics to monitor.mp4

02:47

04 - ML production data best practices.mp4

01:58

05 - Alerts and thresholds for ML.mp4

03:41

06 - Tools and technologies for monitoring.mp4

00:55

05 - 4. Drift Management

01 - Introduction to model drift.mp4

04:31

02 - Concept drift basics.mp4

02:22

03 - Managing concept drift.mp4

02:16

04 - Feature drift basics.mp4

03:03

05 - Managing feature drift.mp4

02:03

06 - 5. Responsible AI

01 - Elements of responsible AI.mp4

03:24

02 - Explainable AI.mp4

02:23

03 - Fairness in ML.mp4

03:00

04 - Security of ML assets.mp4

02:43

05 - Privacy in machine learning.mp4

02:35

07 - Conclusion

01 - Continuing on with MLOps.mp4

00:32

Description

Machine learning operations (MLOps) is one of the fastest growing subfields of artificial intelligence. As more and more models have been deployed in production, the need for a structured, agile, end-to-end, automated machine learning lifecycle has continued to grow. In this course, instructor Kumaran Ponnambalam shows you how to apply key concepts from MLOps to create structured, improved outcomes in your everyday workflow.

Explore the fundamentals of MLOps to get up and running on your next machine learning project. Find out why so many data scientists, engineers, and project managers are so excited about ML models, as you discover the ins and outs of successfully deploying and monitoring models on your own. From continuous delivery to model serving and continuous monitoring to drift management, Kumaran equips you with the skills you need to start practicing effective, fair, explainable, and responsible artificial intelligence.

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Machine Learning

Software Development

Kumaran Ponnambalam

Instructor's Courses

A seasoned veteran in everything data, with a reputation for delivering high performance database and SaaS applications and currently specializing in leading Big Data Science and Engineering efforts

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