An Improved and Functional Design of End-to-End Machine Learning Life Cycle

Explore the comprehensive end-to-end machine learning life cycle in this 27-minute video from Prodramp. Learn about the four essential stages: data preparation, model training and tuning, model deployment and monitoring, and inference or model serving. Discover the functional design of each stage, including internal steps and their importance in solving both small business problems and large-scale challenges. Gain insights into data preparation techniques, model training strategies, deployment best practices, and effective monitoring methods. Understand the significance of model retraining and how to implement a robust ML pipeline. Perfect for aspiring data scientists, ML engineers, and professionals looking to enhance their understanding of the complete machine learning process.

Video Start
Content intro
Motivation to this video
4 Stages of ML Life cycle
Stage 1: Data Preparation
Stage 2: ML Training and Tuning
Stage 3: Model Deployment and Monitoring
Stage 4: Inference or Model Serving
Monitoring and Re-training
Recap
Credits