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Deployment of Machine Learning Models

Learn how to integrate robust and reliable Machine Learning Pipelines in Production
Instructor:
Soledad Galli
17,146 students enrolled
English [Auto]
Build machine learning model APIs and deploy models into the cloud
Send and receive requests from deployed machine learning models
Design testable, version controlled and reproducible production code for model deployment
Create continuous and automated integrations to deploy your models
Understand the optimal machine learning architecture
Understand the different resources available to productionise your models
Identify and mitigate the challenges of putting models in production

Welcome to Deployment of Machine Learning Models, the most comprehensive machine learning deployments online course available to date. This course will show you how to take your machine learning models from the research environment to a fully integrated production environment.

What is model deployment?

Deployment of machine learning models, or simply, putting models into production, means making your models available to other systems within the organization or the web, so that they can receive data and return their predictions. Through the deployment of machine learning models, you can begin to take full advantage of the model you built.

Who is this course for?

  • If you’ve just built your first machine learning models and would like to know how to take them to production or deploy them into an API,

  • If you deployed a few models within your organization and would like to learn more about best practices on model deployment,

  • If you are an avid software developer who would like to step into deployment of fully integrated machine learning pipelines,

this course will show you how.

What will you learn?

We’ll take you step-by-step through engaging video tutorials and teach you everything you need to know to start creating a model in the research environment, and then transform the Jupyter notebooks into production code, package the code and deploy to an API, and add continuous integration and continuous delivery. We will discuss the concept of reproducibility, why it matters, and how to maximize reproducibility during deployment, through versioning, code repositories and the use of docker. And we will also discuss the tools and platforms available to deploy machine learning models.

Specifically, you will learn:

  • The steps involved in a typical machine learning pipeline

  • How a data scientist works in the research environment

  • How to transform the code in Jupyter notebooks into production code

  • How to write production code, including introduction to tests, logging and OOP

  • How to deploy the model and serve predictions from an API

  • How to create a Python Package

  • How to deploy into a realistic production environment

  • How to use docker to control software and model versions

  • How to add a CI/CD layer

  • How to determine that the deployed model reproduces the one created in the research environment

By the end of the course you will have a comprehensive overview of the entire research, development and deployment lifecycle of a machine learning model, and understood the best coding practices, and things to consider to put a model in production. You will also have a better understanding of the tools available to you to deploy your models, and will be well placed to take the deployment of the models in any direction that serves the needs of your organization.

What else should you know?

This course will help you take the first steps towards putting your models in production. You will learn how to go from a Jupyter notebook to a fully deployed machine learning model, considering CI/CD, and deploying to cloud platforms and infrastructure.

But, there is a lot more to model deployment, like model monitoring, advanced deployment orchestration with Kubernetes, and scheduled workflows with Airflow, as well as various testing paradigms such as shadow deployments that are not covered in this course.

Want to know more? Read on…

This comprehensive course on deployment of machine learning models includes over 100 lectures spanning about 10 hours of video, and ALL topics include hands-on Python code examples which you can use for reference and re-use in your own projects.

In addition, we have now included in each section an assignment where you get to reproduce what you learnt to deploy a new model.

So what are you waiting for? Enroll today, learn how to put your models in production and begin extracting their true value.

Introduction

1
Introduction to the course
2
Course curriculum overview
3
Course requirements
4
Setting up your computer
5
Course Material
6
The code
7
Presentations
8
Download Dataset
9
Additional Resources for the required skills
10
How to approach the course

Overview of Model Deployment

1
Deployments of Machine Learning Models
2
Deployment of Machine Learning Pipelines
3
Research and Production Environment
4
Building Reproducible Machine Learning Pipelines
5
Challenges to Reproducibility
6
Streamlining Model Deployment with Open-Source
7
Additional Reading Resources

Machine Learning System Architecture

1
Machine Learning System Architecture and Why it Matters
2
Specific Challenges of Machine Learning Systems
3
Principles for Machine Learning Systems
4
Machine Learning System Architecture Approaches
5
Machine Learning System Component Breakdown
6
Additional Reading Resources

Research Environment - Developing a Machine Learning Model

1
Research Environment - Process Overview
2
Machine Learning Pipeline Overview
3
Feature Engineering - Variable Characteristics
4
Feature Engineering Techniques
5
Feature Selection
6
Training a Machine Learning Model
7
Research environment - second part
8
Code covered in this section
9
Python library versions
10
Data analysis demo - missing data
11
Data analysis demo - temporal variables
12
Data analysis demo - numerical variables
13
Data analysis demo - categorical variables
14
Feature engineering demo 1
15
Feature engineering demo 2
16
Feature selection demo
17
Model training demo
18
Create a Machine Learning Pipeline
19
Score new data with the house price model
20
Scoring new data with our model
21
Research environment - third part
22
Python Open Source for Machine Learning
23
Open Source Libraries for Feature Engineering
24
Feature engineering with open source demo
25
Research environment - fourth part
26
Intro to Object Oriented Programing
27
Inheritance and the Scikit-learn API
28
Create Scikit-Learn compatible transformers
29
Create transformers that learn parameters
30
Feature engineering pipeline demo
31
Should feature selection be part of the pipeline?
32
Research environment - final section
33
Getting Ready for Deployment - Final Pipeline
34
Create and end to end Pipeline for Classification
35
Bonus: Additional Resources on Scikit-Learn

Packaging The Model for Production

1
Introduction to Production Code
2
Repo for this section
3
Code Overview
4
Reminder: Download the Kaggle Data
5
Package Requirements Files
6
Working with tox [Do NOT skip - important]
7
Troubleshooting Tox
8
Package Config
9
The Model Training Script & Pipeline
10
Introduction to Pytest [Optional]
11
Feature Engineering Code in the Package
12
Making Predictions with the Package
13
Building the Package
14
Tooling
15
Section Notes & Further Reading

Serving and Deploying the model via REST API

1
Running the API Locally
2
Understanding the Architecture of the API
3
Introduction to FastAPI
4
The API Endpoints
5
Using Schemas in our API
6
Logging in our Application
7
The Uvicorn Web Server
8
Introducing Heroku and Platform as a Service (PaaS)
9
Deploying our Application to Heroku
10
Understanding the Heroku-Specific Project Files
11
Section Notes & Further Reading

Continuous Integration and Deployment Pipelines

1
Attention !!! - we are updating this section!
2
8.1 - Introduction to CI/CD
3
8.2 - Setting up CircleCI
4
8.3 - Setup Circle CI Config
5
8.4a - Gotchas
6
8.4 - Publishing the Model to Gemfury
7
8.5 - Testing the CI Pipeline
8
8.6 - Wrap Up

Differential Testing

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Certificate of Completion
Deployment of Machine Learning Models
Category:
Data Science
Teacher
Daniel W
Price:
$218.98 $169
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