A Deep Dive into Machine Learning and MLOps Pipeline with Model Registry and Feature Store

Dive deep into the world of machine learning and MLOps pipelines in this comprehensive 58-minute video. Explore key functionalities of modern, scalable, and reliable machine learning pipelines, including model deployment and prediction. Learn about ML training platforms with AutoML, stacking, and blending techniques. Discover the importance of ML observability, monitoring, model registry, model transformation, and feature stores. Gain insights into data preparation, pre-processing, and various machine learning frameworks. Understand the differences between statistical ML and deep learning, and explore MLOps platforms and architecture. Delve into model registry concepts, ONNX model transformation, and the management of data, code, and model artifacts. Conclude with an in-depth look at feature stores and a recap of the covered topics, equipping you with essential knowledge for building robust ML pipelines in enterprise environments.

- Video starts
- Content intro
- Deep Dive Starts
- Data Preparation and Pre-processing
- Machine Learning Frameworks
- ML Training Frameworks
- Statistical ML and Deep Learning
- AutoML & Blending/Stacking
- ML Ops Platforms
- MLOps Architecture
- Model Registry
- Model Transformation ONNX
- Data, Code and Model Artifacts
- Feature Store
- Recap