Deployment of Machine Learning Models from Udemy

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.

What's inside

Learning objectives

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

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

Syllabus

Introduction

Introduction to the course

Course curriculum overview

Course requirements

Setting up your computer

Course Material

The code

Presentations

Download Dataset

Resources to learn machine learning skills

How to approach the course

Overview of Model Deployment

Deployments of Machine Learning Models

Deployment of Machine Learning Pipelines

Research and Production Environment

Building Reproducible Machine Learning Pipelines

Challenges to Reproducibility

Streamlining Model Deployment with Open-Source

Additional Reading Resources

Machine Learning System Architecture

Machine Learning System Architecture and Why it Matters

Specific Challenges of Machine Learning Systems

Principles for Machine Learning Systems

Machine Learning System Architecture Approaches

Machine Learning System Component Breakdown

We will recreate the process of developing a machine learning model, with the aim of putting the model in production. We will use of open source and create in house packages to streamline deployment.

Research Environment - Process Overview

Machine Learning Pipeline Overview

Feature Engineering - Variable Characteristics

Feature Engineering Techniques

Feature Selection

Training a Machine Learning Model

Research environment - second part

Code covered in this section

Python library versions

Data analysis demo - missing data

Data analysis demo - temporal variables

Data analysis demo - numerical variables

Data analysis demo - categorical variables

Feature engineering demo 1

Feature engineering demo 2

Feature selection demo

Model training demo

Create a Machine Learning Pipeline

Score new data with the house price model

Scoring new data with our model

Research environment - third part

Python Open Source for Machine Learning

Open Source Libraries for Feature Engineering

Feature engineering with open source demo

Research environment - fourth part

Intro to Object Oriented Programing

Inheritance and the Scikit-learn API

Create Scikit-Learn compatible transformers

Create transformers that learn parameters

Feature engineering pipeline demo

Should feature selection be part of the pipeline?

Research environment - final section

Getting Ready for Deployment - Final Pipeline

Create and end to end Pipeline for Classification

Bonus: Additional Resources on Scikit-Learn

Convert Jupyter notebooks from the research environment to a production ready Python package.

Introduction to Production Code

Repo for this section

Code Overview

Understanding the Reasoning Behind the Prod Code Structure

Reminder: Download the Kaggle Data

Package Requirements Files

Working with tox [Do NOT skip - important]

Migrating from Tox 3 to Tox 4

Troubleshooting Tox

Package Config

The Model Training Script & Pipeline

Introduction to Pytest [Optional]

Feature Engineering Code in the Package

Making Predictions with the Package

Building the Package

Tooling

Hands On Assignment: Get The Titanic Classification Model Ready for Prod

Section Notes & Further Reading

Understand how to serve model predictions via a REST API & Deploy that API

Running the API Locally

Understanding the Architecture of the API

Introduction to FastAPI

The API Endpoints

Using Schemas in our API

Logging in our Application

The Uvicorn Web Server

Introducing Railway App and Platform as a Service

What Is a Platform as a Service (PaaS)?

Why Use Railway as Our PaaS?

Railway Links

Deploying our ML Application to Railway - Hands On

Limitations to Be Aware Of & Wrap Up

Continuous Integration and Deployment Pipelines

Introduction to CI/CD

Setting up CircleCI

CI/CD Automation Overview Part 1

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Builds a strong foundation for deploying Machine Learning models for software developers

Taught by Christopher Samiullah and Soledad Galli, who are recognized for their work in Machine Learning

Develops skills, knowledge, and tools that are highly relevant to industry

Covers unique perspectives and ideas that may add color to other topics and subjects

Teaches skills, knowledge, and tools that are useful for personal growth and development

Explores Machine Learning deployment using open-source tools and platforms

Reviews summary

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Deployment of Machine Learning Models with these activities:

Review key concepts from introductory machine learning

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Refresh your understanding of fundamental machine learning principles to prepare for more advanced topics in model deployment.

Browse courses on Machine Learning Fundamentals

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Go over notes or textbooks from a previous introductory machine learning course.
Complete practice questions or quizzes to test your knowledge.

Complete 10 beginner-level Jupyter notebook challenges

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Practice coding problems and tutorials to gain hands-on experience with Jupyter notebooks and basic machine learning concepts.

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Find 10 beginner-level Jupyter notebook challenges online.
Create a new Jupyter notebook.
Solve the challenge and test your code.

Follow a tutorial on creating a simple machine learning pipeline

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Gain practical experience in constructing a machine learning pipeline to prepare for the deployment process.

Browse courses on Machine Learning Pipelines

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Find a tutorial on creating a simple machine learning pipeline.
Follow the tutorial step-by-step, coding along the way.
Run the pipeline and analyze the results.

Five other activities

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Join a study group to discuss model deployment concepts

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Engage in discussions with peers to deepen your understanding and exchange ideas related to model deployment.

Browse courses on Machine Learning Deployment

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Find or create a study group with peers enrolled in the course.
Meet regularly to discuss the course material, ask questions, and share insights.

Attend a workshop on cloud-based model deployment

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Expand your knowledge and network by attending a workshop that focuses on cloud-based model deployment techniques.

Browse courses on Model Deployment

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Find a reputable workshop on cloud-based model deployment.
Register and attend the workshop.
Participate in interactive exercises and discussions.

Contribute to an open-source project related to model deployment

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Gain hands-on experience in real-world model deployment scenarios and contribute to the broader machine learning community.

Browse courses on Model Deployment

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Identify an open-source project related to model deployment.
Familiarize yourself with the project's codebase and documentation.
Identify a bug or feature to work on.
Submit a pull request with your proposed changes.

Write a blog post or article on best practices in model deployment

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Solidify your understanding and share your knowledge by creating a detailed guide on best practices in model deployment.

Browse courses on Model Deployment

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Choose a specific aspect of model deployment to focus on.
Research best practices and gather insights from experts.
Write a well-structured blog post or article.
Publish your content online and share it with others.

Develop a small-scale model deployment project

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Apply your knowledge by creating a working model deployment solution that addresses a specific problem.

Browse courses on Model Deployment

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Define a problem statement and gather data.
Train a machine learning model and evaluate its performance.
Deploy the model and evaluate its performance in the real world.
Write a report or presentation on your project.

Career center

Learners who complete Deployment of Machine Learning Models will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Deployment of Machine Learning Models helps refine skills of building, testing, and maintaining machine learning models, which will aid in your success as a Machine Learning Engineer. By exploring techniques and various tools, you'll gain a comprehensive understanding of serving your models. With the support of this course's lessons, you can confidently take models beyond research to gain actionable insights.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

This course will prepare you to transition into the role of a Data Scientist by enhancing your technical capabilities. With hands-on experiences, you will have expertise in deploying machine learning models from research to production, optimizing real-world business outcomes. The course's lessons on CI/CD, advanced deployment orchestration, and scheduled workflows will equip you to effectively navigate challenges in this field.

See salaries and explore the career path for Data Scientist

Software Engineer

Advance your career as a Software Engineer with the knowledge gained from Deployment of Machine Learning Models. You'll master writing testable, version-controlled, and reproducible production code for model deployment. The course curriculum covers object-oriented programming and open-source technologies. By understanding best practices in coding and model deployment, you'll elevate your software engineering capabilities.

See salaries and explore the career path for Software Engineer

Machine Learning Researcher

Enhance your research capabilities in machine learning by taking Deployment of Machine Learning Models. This course provides a systematic approach to transitioning models from research to production. You'll gain skills in packaging code and deploying it to APIs, enabling you to evaluate and refine models in real-world scenarios. The emphasis on reproducibility and version control will strengthen your research foundation.

See salaries and explore the career path for Machine Learning Researcher

Data Analyst

As a Data Analyst, you can elevate your career by taking Deployment of Machine Learning Models. This course covers transforming Jupyter notebooks into production code, helping you bridge the gap between model development and implementation. You'll learn to create Python packages, deploy models, and use docker for version control, which are essential skills for streamlining data analysis pipelines.

See salaries and explore the career path for Data Analyst

Cloud Architect

This course may be useful for Cloud Architects seeking to expand their expertise in deploying machine learning models. You'll gain insights into cloud platforms and infrastructure, enabling you to make informed decisions about model deployment. By understanding best practices in CI/CD and continuous integration, you can optimize your cloud architecture for efficient model deployment.

See salaries and explore the career path for Cloud Architect

Data Engineer

Deployment of Machine Learning Models may be helpful for Data Engineers. It covers essential concepts of model deployment, such as creating Python packages and deploying models into a realistic production environment. By understanding the challenges of putting models into production, you can develop robust and scalable data pipelines that seamlessly integrate machine learning models.

See salaries and explore the career path for Data Engineer

Business Analyst

This course may be useful for Business Analysts seeking to enhance their understanding of machine learning model deployment. You'll gain insights into the machine learning system architecture, enabling you to effectively communicate with technical teams and stakeholders. By understanding the challenges and opportunities of model deployment, you can contribute to informed decision-making and drive business value.

See salaries and explore the career path for Business Analyst

Product Manager

Product Managers can benefit from Deployment of Machine Learning Models. The course provides a comprehensive overview of the machine learning deployment lifecycle, enabling you to understand the technical aspects of model deployment. By gaining insights into model reproducibility, versioning, and continuous integration, you can effectively manage product development and ensure successful implementation of machine learning solutions.

See salaries and explore the career path for Product Manager

Quantitative Analyst

This course may be useful for Quantitative Analysts looking to expand their knowledge in machine learning model deployment. You'll gain insights into model evaluation and deployment, enabling you to make informed decisions about model selection and implementation. By understanding the challenges of model deployment, you can develop robust and reliable quantitative models that drive data-driven decision-making.

See salaries and explore the career path for Quantitative Analyst

Software Developer

Deployment of Machine Learning Models may be helpful for Software Developers. It covers essential concepts of transforming Jupyter notebooks into production code and packaging the code into a Python package. By understanding the principles of testable, version-controlled, and reproducible production code, you can contribute to the development of robust and maintainable software.

See salaries and explore the career path for Software Developer

Statistician

This course may be useful for Statisticians seeking to enhance their knowledge in machine learning model deployment. You'll gain insights into the machine learning system architecture, enabling you to effectively interpret and communicate statistical findings. By understanding the challenges and opportunities of model deployment, you can contribute to the development of reliable and impactful statistical models.

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Risk Manager

Deployment of Machine Learning Models may be useful for Risk Managers seeking to gain a practical understanding of model deployment. You'll learn about CI/CD, continuous integration, and continuous delivery, enabling you to implement robust model deployment pipelines. By understanding the challenges of model deployment, you can develop effective risk management strategies and ensure the reliability of deployed models.

See salaries and explore the career path for Risk Manager

Actuary

Deployment of Machine Learning Models may be useful for Actuaries. It covers essential concepts of model reproducibility and version control, enabling you to develop reliable and auditable actuarial models. By understanding how to mitigate challenges of putting models into production, you can contribute to the implementation of robust and transparent actuarial solutions.

See salaries and explore the career path for Actuary

Financial Analyst

This course may be useful for Financial Analysts looking to incorporate machine learning models into their work. You'll gain insights into model evaluation and deployment, enabling you to make informed decisions about model selection and implementation. By understanding the challenges of model deployment, you can develop robust and reliable financial models that drive informed investment decisions.

See salaries and explore the career path for Financial Analyst