Deployment of Machine Learning Models

Deployment of Machine Learning Models, available at $119.99, has an average rating of 4.55, with 219 lectures, 1 quizzes, based on 5502 reviews, and has 38270 subscribers.

You will learn about 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 This course is ideal for individuals who are Data scientists who want to deploy their first machine learning model or Data scientists who want to learn best practices model deployment or Software developers who want to transition into machine learning It is particularly useful for Data scientists who want to deploy their first machine learning model or Data scientists who want to learn best practices model deployment or Software developers who want to transition into machine learning.

Enroll now: Deployment of Machine Learning Models

Summary

Title: Deployment of Machine Learning Models

Price: $119.99

Average Rating: 4.55

Number of Lectures: 219

Number of Quizzes: 1

Number of Published Lectures: 150

Number of Curriculum Items: 224

Number of Published Curriculum Objects: 154

Original Price: $44.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

Data scientists who want to deploy their first machine learning model
Data scientists who want to learn best practices model deployment
Software developers who want to transition into machine learning

Target Audiences

Data scientists who want to deploy their first machine learning model
Data scientists who want to learn best practices model deployment
Software developers who want to transition into machine learning

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to the course

Lecture 2: Course curriculum overview

Lecture 3: Course requirements

Lecture 4: Setting up your computer

Lecture 5: Course Material

Lecture 6: The code

Lecture 7: Presentations

Lecture 8: Download Dataset

Lecture 9: Resources to learn machine learning skills

Lecture 10: How to approach the course

Chapter 2: Overview of Model Deployment

Lecture 1: Deployments of Machine Learning Models

Lecture 2: Deployment of Machine Learning Pipelines

Lecture 3: Research and Production Environment

Lecture 4: Building Reproducible Machine Learning Pipelines

Lecture 5: Challenges to Reproducibility

Lecture 6: Streamlining Model Deployment with Open-Source

Lecture 7: Additional Reading Resources

Chapter 3: Machine Learning System Architecture

Lecture 1: Machine Learning System Architecture and Why it Matters

Lecture 2: Specific Challenges of Machine Learning Systems

Lecture 3: Principles for Machine Learning Systems

Lecture 4: Machine Learning System Architecture Approaches

Lecture 5: Machine Learning System Component Breakdown

Lecture 6: Additional Reading Resources

Chapter 4: Research Environment – Developing a Machine Learning Model

Lecture 1: Research Environment – Process Overview

Lecture 2: Machine Learning Pipeline Overview

Lecture 3: Feature Engineering – Variable Characteristics

Lecture 4: Feature Engineering Techniques

Lecture 5: Feature Selection

Lecture 6: Training a Machine Learning Model

Lecture 7: Research environment – second part

Lecture 8: Code covered in this section

Lecture 9: Python library versions

Lecture 10: Data analysis demo – missing data

Lecture 11: Data analysis demo – temporal variables

Lecture 12: Data analysis demo – numerical variables

Lecture 13: Data analysis demo – categorical variables

Lecture 14: Feature engineering demo 1

Lecture 15: Feature engineering demo 2

Lecture 16: Feature selection demo

Lecture 17: Model training demo

Lecture 18: Scoring new data with our model

Lecture 19: Research environment – third part

Lecture 20: Python Open Source for Machine Learning

Lecture 21: Open Source Libraries for Feature Engineering

Lecture 22: Feature engineering with open source demo

Lecture 23: Research environment – fourth part

Lecture 24: Intro to Object Oriented Programing

Lecture 25: Inheritance and the Scikit-learn API

Lecture 26: Create Scikit-Learn compatible transformers

Lecture 27: Create transformers that learn parameters

Lecture 28: Feature engineering pipeline demo

Lecture 29: Should feature selection be part of the pipeline?

Lecture 30: Research environment – final section

Lecture 31: Getting Ready for Deployment – Final Pipeline

Lecture 32: Bonus: Additional Resources on Scikit-Learn

Chapter 5: Packaging The Model for Production

Lecture 1: Introduction to Production Code

Lecture 2: Repo for this section

Lecture 3: Code Overview

Lecture 4: Understanding the Reasoning Behind the Prod Code Structure

Lecture 5: Reminder: Download the Kaggle Data

Lecture 6: Package Requirements Files

Lecture 7: Working with tox [Do NOT skip – important]

Lecture 8: Migrating from Tox 3 to Tox 4

Lecture 9: Troubleshooting Tox

Lecture 10: Package Config

Lecture 11: The Model Training Script & Pipeline

Lecture 12: Introduction to Pytest [Optional]

Lecture 13: Feature Engineering Code in the Package

Lecture 14: Making Predictions with the Package

Lecture 15: Building the Package

Lecture 16: Tooling

Lecture 17: Section Notes & Further Reading

Chapter 6: Serving and Deploying the model via REST API

Lecture 1: Running the API Locally

Lecture 2: Understanding the Architecture of the API

Lecture 3: Introduction to FastAPI

Lecture 4: The API Endpoints

Lecture 5: Using Schemas in our API

Lecture 6: Logging in our Application

Lecture 7: The Uvicorn Web Server

Lecture 8: Introducing Railway App and Platform as a Service

Lecture 9: What Is a Platform as a Service (PaaS)?

Lecture 10: Why Use Railway as Our PaaS?

Lecture 11: Railway Links

Lecture 12: Deploying our ML Application to Railway – Hands On

Lecture 13: Limitations to Be Aware Of & Wrap Up

Lecture 14: Section Notes & Further Reading

Chapter 7: Continuous Integration and Deployment Pipelines

Lecture 1: Introduction to CI/CD

Lecture 2: Setting up CircleCI

Lecture 3: CI/CD Automation Overview Part 1

Instructors

Soledad Galli
Data scientist | Instructor | Software developer
Christopher Samiullah
Machine Learning Engineer
Train in Data Team
Data scientists | Instructors | Software engineers

Rating Distribution

1 stars: 102 votes
2 stars: 103 votes
3 stars: 499 votes
4 stars: 1773 votes
5 stars: 3025 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

Menu