Machine Learning in Production

Machine Learning in Production

Summary

In this 1-day course, machine learning engineers, data engineers, and data scientists learn the best practices for managing the complete machine learning lifecycle from experimentation and model management through various deployment modalities and production issues. Students begin with end-to-end reproducibility of machine learning models using MLflow including data management, experiment tracking, and model management before deploying models with batch, streaming, and real-time as well as addressing related monitoring, alerting, and CI/CD issues. Sample code accompanies all modules and theoretical concepts.

Description

First, this course explores managing the experimentation process using MLflow with a focus on end-to-end reproducibility including data, model, and experiment tracking. Second, students operationalize their models by integrating with various downstream deployment tools including saving models to the MLflow model registry, managing artifacts and environments, and automating the testing of their models. Third, students implement batch, streaming, and real-time deployment options. Finally, additional production issues including continuous integration, continuous deployment are covered as well as monitoring and alerting.

By the end of this course, you will have built an end-to-end pipeline to log, deploy, and monitor machine learning models. This course is taught entirely in Python.

Duration

8 hours

Objectives

Upon completion, students should be able to:

  • Track data and machine learning experiments to organize the machine learning life cycle
  • Create, organize, and package machine learning projects with a focus on reproducibility and using a model registry to collaborate with a team
  • Develop a generalizable way of handling machine learning models created in and deployed to a variety of environments
  • Deploy basic CI/CD infrastructure using webhooks
  • 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 various statistically rigorous solutions to drift and implement basic retraining methods

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

Outline

Day 1 AM

Time

Lesson                                                                      

Description                                                                                                                                                                                                                                                  

40m

Introductions, Setup & MLflow Overview

Registration, Courseware & Q&As

20m

Data Management

Design patterns to manage data lineage for data reproducibility

10m

Break


30m

Experiment Tracking

Tracking experiments to organize the machine learning life cycle

30m

Advanced Experiment Tracking

Advanced methods for tracking ML experiments

10m

Break


20m

Model Management

Creating, organizing, and packaging machine learning projects with pre-processing code

20m

Model Registry

Artifact management for production models

20m

Webhooks and Testing

Integrating MLflow webhooks into the Model Registry


Day 1 PM

Time

Lesson                                                                      

Description                                                                                                                                                                                                                                                  

30m

Production Issues

Various production issues encountered in deploying and monitoring machine learning models

30m

Batch Deployment

Various strategies for deploying models using batch including pure Python, Spark, and on the JVM

10m

Break


20m

Streaming Deployment

How to perform inference on a stream of incoming data

40m

Real Time Deployment

Real time deployment with a focus on RESTful services


10m

Break


30m

CI/CD

Continuous Integration, Continuous Deployment of ML models


20m

Drift Monitoring

Explore solutions to concept and data drift

10m

Alerting

Alerting strategies using email and REST integration

Upcoming Classes

Date
Time
Location
Price
Aug 17 - 18
8:00 AM - 12:00 PM
Australian Eastern Standard Time (Victoria)
Online - Virtual - Australia
$ 1000.00 USD
Sep 20
9:00 AM - 5:00 PM
Central European Summer Time
Online - Virtual - EMEA
$ 1000.00 USD
Oct 6 - 7
9:00 AM - 1:00 PM
Pacific Daylight Time
Online - Virtual - Americas (half-day schedule)
$ 1000.00 USD
Oct 20
9:00 AM - 5:00 PM
Central European Summer Time
Online - Virtual - EMEA
$ 1000.00 USD
Nov 17 - 18
9:00 AM - 1:00 PM
Pacific Standard Time
Online - Virtual - Americas (half-day schedule)
$ 1000.00 USD
Nov 24
9:00 AM - 5:00 PM
Central European Time
Online - Virtual - EMEA
$ 1000.00 USD
Dec 17
9:00 AM - 5:00 PM
Central European Time
Online - Virtual - EMEA
$ 1000.00 USD
Dec 28 - 29
9:00 AM - 1:00 PM
Pacific Standard Time
Online - Virtual - Americas (half-day schedule)
$ 1000.00 USD

Onsite Training

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Public Training

Virtual - Australia

Virtual - EMEA

  • Confirmed
    9:00 AM - 5:00 PM CEST
    $ 1000.00 USD
  • 9:00 AM - 5:00 PM CEST
    $ 1000.00 USD
  • 9:00 AM - 5:00 PM CET
    $ 1000.00 USD
  • 9:00 AM - 5:00 PM CET
    $ 1000.00 USD

Virtual - Americas (half-day schedule)


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