Machine Learning in Production: MLflow and Model Deployment

Machine Learning in Production: MLflow and Model Deployment

Summary

In this 1-day course, data scientists and data engineers learn best practices for managing experiments, projects, and models using MLflow. Students build a pipeline to log and deploy machine learning models, as well as explore common production issues faced when deploying machine learning solutions and monitoring these models once they have been deployed into production.

Description

 

This course is separated into two main components. The first uses MLflow as the backbone for machine learning development and production. This includes tracking the machine learning lifecycle, packaging projects for deployment, using the MLflow model registry, and more. The second component looks at various production issues, the four main deployment paradigms, monitoring, and alerting. Depending on the desires of the class, numerous electives are also available on the various MLflow functionality and deployment scenarios.

 

By the end of this course, you will have built the infrastructure to manage the development, deployment, and monitoring of models in production. This course is taught entirely in Python.

Duration

8 hours

Objectives

Upon completion, students should be able to:

  • Track machine learning experiments to organize the machine learning life cycle
  • Create, organize, and package machine learning projects with a focus on reproducibility and collaborating with a team
  • Develop a generalizable way of handling machine learning models created in and deployed to a variety of environments
  • Explore the various production issues encountered in deploying and monitoring machine learning models
  • Introduce various strategies for deploying models using batch, streaming, and real-time
  • Explore solutions to drift and implement a basic retraining method and two ways of dynamically monitoring drift

Audience

  • Data Scientist

  • Machine Learning Engineer

  • Data Engineer

Prerequisites

  • Intermediate experience using Python/pandas
  • Working knowledge of machine learning and data science (scikit-learn, TensorFlow, etc.)
  • Familiarity with Apache Spark
  • Basic familiarity with object storage, databases, and networking

 

Upcoming Classes

Date
Time
Location
Price
Nov 30 - Dec 1
9:00 AM - 1:00 PM
Pacific Standard Time
Online - Virtual - Americas (half-day schedule)
$ 1000.00 USD