Dive into the world of MLOps with our comprehensive video series designed to transform your understanding of Machine Learning Operations. From foundational concepts to advanced deployment strategies, each episode is packed with insights and practical tools to help you master the MLOps lifecycle. Whether you're a data scientist, ML engineer, or simply curious about the industry, this playlist will equip you with the knowledge you need to excel in the fast-evolving landscape of AI and data science.
0:00:49 - 1 Introduction to MLOps
0:13:20 - 2 MLOps Lifecycle Toolkit
0:24:05 - 3 Introduction to MLOps
0:32:36 - 4 Foundations for MLOps Systems
0:48:55 - 5 Introduction to MLOps and Foundations
0:56:44 - 6 Tools and Techniques for Implementing MLOps
1:06:00 - 7 Introduction to Infrastructure for MLOps
1:17:53 - 8 Containerization for Data Scientists
1:26:47 - 9 Introduction to Building Training Pipelines
1:36:51 - 10 Experiment Tracking with MLFlow
1:45:29 - 11 Introduction to Inference Pipelines
1:58:55 - 12 Advanced Concepts in Inference
2:09:39 - 13 Introduction to the Spiral MLOps Lifecycle
2:20:38 - 14 Deployment Models in Data Science
2:33:17 - 15 Introduction to Data Ethics
2:42:39 - 16 Advanced Considerations in Data Ethics
2:51:21 - 17 Applications of Data Science Across Industries
3:03:01 - 18 The Evolving Role of Domain Experts in Data Science
3:14:07 - 19 Introduction to Machine Learning Engineering
3:24:27 - 20 What does an ML solution look like?
3:34:51 - 21 Overview of ML Engineering
3:44:44 - 22 Setting Up Development Processes
3:54:09 - 23 Defining the model factory
4:03:07 - 24 Building the model factory with pipelines
4:16:09 - 25 Overview of Machine Learning Solutions
4:24:01 - 26 Best Practices for Packaging Python Code
4:33:51 - 27 Introduction to Machine Learning Deployment
4:43:15 - 28 Introduction to MLOps and Model Deployment
4:53:36 - 29 Packaging and Deploying Models as a Service
5:04:49 - 30 Managing Model Versions and Rollbacks
5:11:56 - 31 ntroduction to MLOps and Model Deployment
5:21:59 - 32 In-depth Review of Monitoring and Logging
5:33:26 - 33 Cost Optimization Strategies in OpenShift
5:42:15 - 34 Introduction to Machine Learning with Red Hat OpenShift
5:51:46 - 35 End-to-End Workflow for Face Detection
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