Lesson 1
Introduction to Reproducible Model Workflows
Dive into reproducible model workflows and machine learning operations, learning about use cases, its history, and what you'll build at the end of the course.
Building a Reproducible Model Workflow
Course
This course empowers the students to be more efficient, effective, and productive in modern, real-world ML projects by adopting best practices around reproducible workflows. 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.
This course empowers the students to be more efficient, effective, and productive in modern, real-world ML projects by adopting best practices around reproducible workflows. 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.
Advanced
4 weeks
Real-world Projects
Completion Certificate
Last Updated November 15, 2023
Machine learning configuration management • Exploratory data analysis • Weights & biases • Data cleaning
Jupyter notebooks • Intermediate Python
Course Lessons
Lesson 2
Machine Learning Pipelines
Build out machine learning pipelines, as well as learning how to version data and model artifacts.
Lesson 3
Data Exploration and Preparation
Come up with re-usable processes for performing exploratory data analysis (EDA), cleaning and pre-processing data, and segregating/splitting data.
Lesson 4
Data Validation
Validate data through deterministic and non-deterministic testing, and look at handling different parameters with PyTest.
Lesson 5
Training, Validation and Experiment Tracking
Write an inference pipeline, validate and choose your best performing models from experiments, and test your final model artifacts.
Lesson 6
Final Pipeline, Release and Deploy
Write a full end-to-end pipeline, release the pipeline, and deploy with MLflow.
Lesson 7 • Project
Build an ML Pipeline for Short-term Rental Prices in NYC
Create a re-usable end-to-end pipeline for predicting short-term rental prices in New York City!
Taught By The Best
Giacomo Vianello
Principal Data Scientist
Giacomo Vianello is an end-to-end data scientist with a passion for state-of-the-art but practical technical solutions. He is Principal Data Scientist at Cape Analytics, where he develops AI systems to extract intelligence from geospatial imagery bringing, cutting-edge AI solutions to the insurance and real estate industries.
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