Gain a comprehensive understanding of the machine learning life cycle in this 38-minute technical overview video. Explore the four essential stages: data preparation, model training and tuning, model deployment and monitoring, and inference or model serving. Delve into detailed explanations of feature engineering, feature stores, data artifacts, and online feature stores. Learn about the importance of MLI (Machine Learning Interpretability) in model deployment and monitoring. Discover how these stages apply to both small business problems and large-scale machine learning projects. Enhance your skills as an AI engineer with practical insights and best practices for implementing each stage of the ML life cycle.
Overview
Syllabus
Video Start
Content intro
Motivation to this video
Stage 1: Data Preparation
Data Preparation - Feature Engineering
Data Preparation - Feature Store
Data Preparation - Data Artifacts
Stage 2: Model Training and Tuning
Stage 3: Model Deployment and Monitoring
Model Deployment and Monitoring - Online Feature Store
Model Deployment and Monitoring - MLI
Recap
Credits
Taught by
Prodramp
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