Building a Reproducible Model Workflow

Overview

In particular, it teaches the fundamentals of MLops and how to: a) create a clean, organized, reproducible, end-to-end machine learning pipeline from scratch using MLflow b) clean and validate the data using pytest c) track experiments, code, and results using GitHub and Weights & Biases d) select the best-performing model for production and e) deploy a model using MLflow. Along the way, it also touches on other technologies like Kubernetes, Kubeflow, and Great Expectations and how they relate to the content of the class. The Building a Reproducible Model Workflow program is offered by Udacity.

Course Skills

• Building a Reproducible Model Workflow

Machine learning configuration management • Exploratory data analysis • Weights & biases • Data cleaning • Model deployment • Hydra • Data versioning • Non-deterministic data testing • Machine learning pipeline creation • Deterministic data testing • Pytest • MLflow • Data validation • Model testing • Machine learning experiment tracking • Data pre-processing for ML • Model evaluation • Inference pipelines • Data splitting • Model performance metrics