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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 December 5, 2023

Skills you'll learn:
Machine learning configuration management • Exploratory data analysis • Weights & biases • Data cleaning
Prerequisites:
Jupyter notebooks • Intermediate Python

Course Lessons

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.

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

Photo of Giacomo Vianello

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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Building a Reproducible Model Workflow

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  • Unlimited access to our top-rated courses
  • Real-world projects
  • Personalized project reviews
  • Program certificates
  • Proven career outcomes

4 Months

Average time to complete a Nanodegree program

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Discount applies to the first 4 months of membership, after which plans are converted to month-to-month.