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2021-01-30 19:24:56
30-Day Money-Back Guarantee
Development Data Science Machine Learning

Deployment of Machine Learning Models

Build Machine Learning Model APIs
Rating: 4.4 out of 54.4 (2,532 ratings)
15,427 students
Created by Soledad Galli, Christopher Samiullah
Last updated 2/2021
English
English [Auto]
30-Day Money-Back Guarantee

What you'll 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
Curated for the Udemy for Business collection

Course content

15 sections • 146 lectures • 9h 14m total length

  • Preview06:01
  • Preview08:04
  • Preview03:30
  • Course Pacing and Practice
    04:45
  • Course Tips
    04:09
  • Guidelines on how to approach the course
    02:19
  • Installing Python in your computer
    00:31
  • Slides covered in this course
    00:04
  • Notes covered in this course
    00:09
  • FAQ: Where can I learn more about the required skills?
    00:47

  • Machine Learning Pipeline: Overview
    07:12
  • Machine Learning Pipeline: Feature Engineering
    08:01
  • Machine Learning Pipeline: Feature Selection
    10:05
  • Machine Learning Pipeline: Model Building
    03:01
  • Jupyter notebooks covered in this section
    00:25
  • Note on library versions - DO NOT SKIP
    00:12
  • Download the data set
    00:30
  • Data Analysis - Demo
    17:46
  • Feature Engineering - Demo
    12:12
  • Feature Selection - Demo
    03:52
  • Model Building - Demo
    04:23
  • Getting Ready for Deployment - Demo
    07:50
  • Bonus: Machine Learning Pipeline: Additional Resources
    02:12
  • Randomness in Machine Learning - Setting the Seed
    01:11
  • FAQ: Where can I learn more about the pipeline steps?
    00:14
  • Develop a Machine Learning Pipeline for Classification
    1 question

  • Machine Learning System Architecture and Why it Matters
    02:00
  • Specific Challenges of Machine Learning Systems
    05:57
  • Machine Learning System Approaches
    05:04
  • Machine Learning System Component Breakdown
    05:56
  • Building a Reproducible Machine Learning Pipeline
    10:29
  • Challenges to Reproducibility - OPTIONAL
    00:04
  • Architecture to Minimise Reproducibility Challenges
    00:04
  • Additional Reading Resources
    00:16

  • Production Code: Overview
    02:45
  • Procedural Programming Pipeline
    11:41
  • Procedural Programing: House Prices Demo
    10:13
  • Assignment: Procedural Programming
    1 question
  • Designing a Custom Pipeline
    06:52
  • Designing a Custom Pipeline | Demo Files
    02:09
  • Custom Pipeline | Processing steps
    09:44
  • Custom Pipeline| Fit and Transform
    06:15
  • Executing the Custom Pipeline
    03:11
  • Leveraging a Third Party Pipeline: Scikit-Learn
    08:35
  • Shallow Dive into Scikit-learn API
    04:17
  • Note on library versions - DO NOT SKIP
    00:13
  • Third Party Pipeline: Demo Files
    01:59
  • Scikit-Learn compatible Transformers
    12:37
  • Executing the Deployment Pipeline
    06:43
  • Third Party Pipeline: Closing Remarks
    01:57
  • Production Code - Third Party Pipeline
    1 question
  • Bonus: Additional Resources on Scikit-Learn
    00:10
  • BONUS: Open Source Libraries for Feature Engineering
    04:58
  • BONUS: Should feature selection be part of the pipeline?
    05:56
  • Bonus: Resources to Improve as a Python Developer
    00:12

  • Section 5.1 - Introduction
    01:55
  • Section 5.2 - Installing and Configuring Git
    03:37
  • Section 5.3 - How to Use the Course Resources, Monorepos + Git Refresher
    03:46
  • Our Github repository
    00:25
  • Section5.3b - Opening Pull Requests
    04:01
  • Section5.3c - Primer on Monorepos
    01:53
  • Section 5.4a - Operating System Differences and Potential Gotchas
    01:46
  • Section 5.4b - System Path and Pythonpath Demo
    02:38
  • Section 5.5a - Quick Word for More Advanced Students
    00:42
  • Section5.5b - Virtualenv Introduction
    08:21
  • Section5.5c - Requirements files Introduction
    02:25
  • Section5.5d - Virtualenv refresher
    02:37
  • Section 5.6 - Text Editors / IDEs
    01:24
  • Section 5.7 - Engineering and Python Best Practices
    05:09
  • Quick Note About the Next 2 Lectures
    00:11
  • Section 5.8 - Introduction to Pytest
    11:49
  • Section 5.9 - Introduction to Tox [DO NOT SKIP]
    05:47
  • Section 5.10 - Wrap Up
    01:04

  • 6.1 - Introduction
    02:21
  • 6.1B - GOTCHA FOR STUDENTS ENROLLED PRIOR TO April 04, 2020
    00:40
  • 6.1C - Don't forget to download the data from Kaggle
    00:07
  • Repo for this section
    00:11
  • 6.2 - Training the Model
    08:05
  • 6.3 - Connecting the Pipeline
    03:45
  • 6.4 - Making Predictions with the Model
    04:26
  • 6.5 - Data Validation in the Model Package
    04:09
  • 6.6 - Feature Engineering in the Pipeline
    03:27
  • 6.7 - Versioning and Logging
    11:34
  • 6.8 - Building the Package
    08:17
  • 6.9 - Wrap Up
    01:55

  • 7.1 - Introduction
    03:18
  • 7.2 - Creating the API Skeleton
    04:35
  • 7.2b - Note On Flask
    00:18
  • 7.3 - Adding Config and Logging
    04:10
  • 7.4 - Adding the Prediction Endpoint
    04:09
  • 7.5 - Adding a Version Endpoint
    02:05
  • 7.6 - API Schema Validation
    07:19
  • 7.7 - Wrap Up
    01:02

  • 8.1 - Introduction to CI/CD
    04:24
  • 8.2 - Setting up CircleCI
    01:26
  • 8.3 - Setup Circle CI Config
    06:23
  • 8.4a - Gotchas
    01:47
  • 8.4 - Publishing the Model to Gemfury
    07:59
  • 8.5 - Testing the CI Pipeline
    05:38
  • 8.6 - Wrap Up
    00:54

  • 9.1 - Introduction
    02:15
  • 9.2 - Setting up Differential Tests
    04:28
  • 9.3 - Differential Tests in CI (Part 1 of 2)
    03:01
  • 9.4 - Differential Tests in CI (Part 2 of 2)
    04:01
  • 9.5 Wrap Up
    01:41

  • 10.1 - Introduction
    04:04
  • 10.2 - Heroku Account Creation
    02:37
  • 10.3a - Heroku Gotchas
    00:45
  • 10.3 - Heroku Config
    04:59
  • 10.4 - Testing the Deployment Manually
    01:32
  • 10.5 - Deploying to Heroku via CI
    03:41
  • 10.6 - Wrap Up
    02:05

Requirements

  • A Python installation
  • A Jupyter notebook installation
  • Python coding skills including pandas and scikit-learn
  • Familiarity with Python environments, OOP and git
  • Familiarity with Machine Learning algorithms
  • This is an intermediate level course (see description)

Description

Learn how to put your machine learning models into production.


What is model deployment?

Deployment of machine learning models, or simply, putting models into production, means making your models available to your other business systems. By deploying models, other systems can send data to them and get their predictions, which are in turn populated back into the company systems. Through machine learning model deployment, you and your business can begin to take full advantage of the model you built.

When we think about data science, we think about how to build machine learning models, we think about which algorithm will be more predictive, how to engineer our features and which variables to use to make the models more accurate. However, how we are going to actually use those models is often neglected. And yet this is the most important step in the machine learning pipeline. Only when a model is fully integrated with the business systems, we can extract real value from its predictions.


Why take this course?

This is the first and only online course where you can learn how to deploy machine learning models. In this course, you will learn every aspect of how to put your models in production. The course is comprehensive, and yet easy to follow. Throughout this course you will learn all the steps and infrastructure required to deploy machine learning models professionally.

In this course, you will have at your fingertips, the sequence of steps that you need to follow to deploy a machine learning model, plus a project template with full code, that you can adapt to deploy your own models.


What is the course structure?

Part 1: The Research Environment

The course begins from the most common starting point for the majority of data scientists: a Jupyter notebook with a machine learning model trained in it.

Part 2: Understanding Machine Learning Systems

An overview of key architecture and design considerations for different types of machine learning models. This part sets the theoretical foundation for the practical part of the course.

Part 3: From Research to Production Code

A hands-on project with complete source code, which takes you through the process of converting your notebooks into production ready code.

Part 4: Deployment Tooling

Continuing with the hands-on project, this section takes you through the necessary tools for real production deployments, like CI/CD, testing, model cloud storage and more.

Part 5: Deployments

In this section, you will deploy models to both cloud platforms (Heroku) and cloud infrastructure (AWS).

Part 6: Bonus sections

In addition, there are dedicated sections which discuss handling big data, deep learning and common issues encountered when deploying models to production.


Important:

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.


Who are the instructors?

We have gathered a fantastic team to teach this course. Sole is a leading data scientist in finance and insurance, with 3+ years of experience in building and implementing machine learning models in the field, and multiple IT awards and nominations. Chris is an AI software engineer with enormous experience in building APIs and deploying machine learning models, allowing business to extract full benefit from their implementation and decisions.


Who is this course for?

This course is suitable for data scientists looking to deploy their first machine learning model, and software developers looking to transition into AI software engineering. Deployment of machine learning models is a very advanced topic in the data science path so the course will also be suitable for intermediate and advanced data scientists.


How advanced is this course?

This is an intermediate level course, and it requires you to have experience with Python programming and git. How much experience? It depends on how much time you would like to set aside to go ahead and learn those concepts that are new to you. To give you an example, we will work with Python environments, we will work with object oriented programming, we will work with the command line to run our scripts, and we will checkout code at different stages with git. You don’t need to be an expert in all of these topics, but it will certainly help if you have heard of them, and worked with them before.

For those relatively new to software engineering, the course may be challenging. We have added detailed lecture notes and references, so we do believe beginners can take the course, but keep in mind that you will need to put in the hours to read up on unfamiliar concepts. On this point, the course slowly increases in complexity, so you can see how we pass, gradually, from the familiar Jupyter notebook, to the less familiar production code, using a project-based approach which we believe is optimal for learning. It is important that you follow the code, as we build up on it.


Still not sure if this is the right course for you?

Here are some rough guidelines:

Never written a line of code before: This course is unsuitable

Never written a line of Python before: This course is unsuitable

Never trained a machine learning model before: This course is unsuitable. Ideally, you have already built a few machine learning models, either at work, or for competitions or as a hobby.

Have only ever operated in the research environment: This course will be challenging, but if you are ready to read up on some of the concepts we will show you, the course will offer you a great deal of value.

Have a little experience writing production code: There may be some unfamiliar tools which we will show you, but generally you should get a lot from the course.

Non-technical: You may get a lot from just the theoretical section (section 3) so that you get a feel for the possible architectures and challenges of ML deployments. The rest of the course will be a stretch.


To sum up:

With more than 50 lectures and 8 hours of video this comprehensive course covers every aspect of model deployment. Throughout the course you will use Python as your main language and other open source technologies that will allow you to host and make calls to your machine learning models.


We hope you enjoy it and we look forward to seeing you on board!

Who this course is for:

  • Intermediate and advanced data scientists
  • Software developers who want to transition into machine learning
  • Intermediate data scientists who want to deploy their first machine learning model
  • Machine Learning practicioners who want to learn best practices around model deployment

Featured review

Ron Medina
Ron Medina
8 courses
5 reviews
Rating: 5.0 out of 52 months ago
Course content is top notch... This is easily worth 10x what I've paid for it. The course content is essential for machine learning engineers, especially those working on individual projects. The audio from Prof. Sollegalli could be improved (I can hear her saliva when she speaks -- though I am able to block it now after many lectures). Her accent also takes a lot of getting used to. But that in itself is a cultural experience that, I guess, adds flavor to the course.

Instructors

Soledad Galli
Lead Data Scientist
Soledad Galli
  • 4.6 Instructor Rating
  • 5,882 Reviews
  • 25,395 Students
  • 6 Courses

Soledad Galli is a lead data scientist and founder of Train in Data. She has experience in finance and insurance, received a Data Science Leaders Award in 2018 and was selected “LinkedIn’s voice” in data science and analytics in 2019. Sole is passionate about sharing knowledge and helping others succeed in data science.

As a data scientist in Finance and Insurance companies, Sole researched, developed and put in production machine learning models to assess Credit Risk, Insurance Claims and to prevent Fraud, leading in the adoption of machine learning in the organizations.

Sole is passionate about empowering people to step into and excel in data science. She mentors data scientists, writes articles online, speaks at data science meetings, and teaches online courses on machine learning.

Sole has recently created Train In Data, with the mission to facilitate and empower people and organizations worldwide to step into and excel in data science and analytics.

Sole has an MSc in Biology, a PhD in Biochemistry and 8+ years of experience as a research scientist in well-known institutions like University College London and the Max Planck Institute. She has scientific publications in various fields such as Cancer Research and Neuroscience, and her research was covered by the media on multiple occasions.

Soledad has 4+ years of experience as an instructor in Biochemistry at the University of Buenos Aires, taught seminars and tutorials at University College London, and mentored MSc and PhD students at Universities.

Feel free to contact her on LinkedIn.


========================


Soledad Galli es científica de datos y fundadora de Train in Data. Tiene experiencia en finanzas y seguros, recibió el premio Data Science Leaders Award en 2018 y fue seleccionada como "la voz de LinkedIn" en ciencia y análisis de datos en 2019. A Soledad le apasiona compartir conocimientos y ayudar a otros a tener éxito en la ciencia de datos.


Como científica de datos en compañías de finanzas y seguros, Sole desarrolló y puso en producción modelos de aprendizaje automático para evaluar el riesgo crediticio, automatizar reclamos de seguros y para prevenir el fraude, facilitando la adopción del aprendizaje de máquina en estas organizaciones.


A Sole le apasiona ayudar a que las personas aprendan y se destaquen en ciencia de datos, es por eso habla regularmente en reuniones de ciencia de datos, escribe varios artículos disponibles en la web y crea cursos sobre aprendizaje de máquina.


Sole ha creado recientemente Train In Data, con la misión de ayudar a las personas y organizaciones de todo el mundo a que aprendan y se destaquen en la ciencia y análisis de datos.


Sole tiene una maestría en biología, un doctorado en bioquímica y más de 8 años de experiencia como investigadora científica en instituciones prestigiosas como University College London y el Instituto Max Planck. Tiene publicaciones científicas en diversos campos, como la investigación contra el Cáncer y la Neurociencia, y sus resultados fueron cubiertos por los medios en múltiples ocasiones.


Soledad tiene más de 4 años de experiencia como instructora de bioquímica en la Universidad de Buenos Aires, dio seminarios y tutoriales en University College London, en Londres, y fue mentora de estudiantes de maestría y doctorado en diferentes universidades.


No dudes en contactarla en LinkedIn.

Christopher Samiullah
Machine Learning Engineer
Christopher Samiullah
  • 4.4 Instructor Rating
  • 2,701 Reviews
  • 16,055 Students
  • 2 Courses

My name is Chris. I'm a professional software engineer from the UK. I've been writing code for 8 years, and for the past three years, I've focused on scaling machine learning applications. I've done this at fintech and healthtech companies in London, where I've worked on and grown production machine learning applications used by hundreds of thousands of people. I've built and maintained machine learning systems which make credit-risk and fraud detection judgements on over a billion dollars of personal loans per year for the challenger bank Zopa. I currently work on systems for predicting health risks for patients around the world at Babylon Health.


In the past, I've worn a variety of hats. I worked at a global healthcare company, Bupa, which included being a core developer on their flagship website, and three years working in Beijing setting up mobile, web and IT for medical centers in China. Whilst in Beijing, I ran the Python meetup group, mentored a lot of junior developers, and ate a lot of dumplings. I enjoy giving talks at engineering meetups, building systems that create value, and writing software development tutorials and guides. I've written on topics ranging from wearable development, to internet security, to Python web frameworks.


I'm passionate about teaching in a way that minimizes the time between "ah hah" moments, but doesn't leave you Googling every other word. Complexity is necessary for application in the real world, but too much complexity is overwhelming and counter-productive. I will help you find the right balance.


Feel free to connect on LinkedIn (very active) or Twitter (getting more active in 2021)

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