Machine Learning School

Many people know how to train Machine Learning models. 

Unfortunately, this is around 5% of the work required to build an end-to-end system.

This program will show you the other 95%.

What Do You Get From Joining?

When you join the program, you get access to the following:

1. Pre-recorded lessons. A group of video lessons focusing on the fundamental aspects of Machine Learning in Production.
2. A 9-hour live cohort. Every month, there are 6 live sessions of 90 minutes each (9 hours total) where we will build an end-to-end Machine Learning system from scratch. You can attend live or watch a recording of the sessions.
3. Assignments. This is a program for builders, and you will have plenty to do. Every session will give you a list of assignments to practice what you learned.
4. A Community. You'll join a community of professionals from every corner of the world with something in common: They are all building Machine Learning systems for a living.

Three things will happen when you finish this program:

1. You'll have a solid understanding of most theoretical aspects concerning Machine Learning systems.
2. You'll have experience building an end-to-end system using AWS SageMaker. You'll understand how to process data, train, tune, evaluate, deploy, and monitor models in a production environment. You'll know a few tricks from somebody who spent many nights trying to figure these things out.
3. You'll build connections with like-minded professionals working in the industry.

Who Is This Program For?

1. This is a hands-on, technical program. Anyone who wants to use Machine Learning to build solutions for real-world problems will benefit from it.
2. This program focuses on designing Machine Learning systems and doesn't cover Machine Learning theory. You will not learn about the differences between Decision Trees and Neural Networks or how a larger learning rate will change your predictions.
3. To get the most out of the program, you should have experience writing software. We use Python, but those who know a different language shouldn't worry too much.
4. Ideally, you have a basic grasp of Machine Learning terminology. You don't need experience building models but should be familiar with the field. For example, you don't need to understand the architecture of a Deep Learning model, but you should understand what "training" a model means.

Schedule and Structure

Every month, the time of the cohort changes. We will meet 3 times for 2 consecutive weeks. Every session will be recorded, so you can attend live or watch the recorded version later.

Here are the upcoming cohorts:

• June 19 - 30, 2023. 10 am CET. (Monday, Wednesday, and Friday)
• July 24 - August 4, 2023. 10 am EST. (Monday, Wednesday, and Friday)

We will start the program with a simple problem and build an entire end-to-end system over the course of the six sessions. Every session is packed with information and code. It will be intense but fun.

Here is the structure of the course:

• Session 1 — Getting Started
  Introduction to the problem and exploring the dataset.
  An introduction to Machine Learning Pipelines in Production.
  Pre-processing the dataset using Scikit-Learning pipelines.
  Creating a Processing Job to transform the dataset.
• Session 2 — Training and Tuning
  An overview of the different ways to train a model in production systems.
  Building a multi-class classification model.
  An introduction to Training and Hyperparameter Tuning Jobs.
  Extending the Pipeline to train the model.
  Extending the Pipeline to tune hyperparameters for the model.
• Session 3 — Model Evaluation
  An overview of evaluating models in production systems.
  Building a script to evaluate the model.
  Extending the Pipeline to evaluate the model.
• Session 4 — Model Registration
  An overview of the model versioning.
  Introduction to the Model Registry.
  Deploying the model manually from the Model Registry.
  Extending the Pipeline with a branch to verify the quality of a model.
  Extending the Pipeline to register a new version of the model.
  Making predictions using transformed data.
• Session 5 - Model Deployment
  Repacking the model with a custom handler.
  Introduction to data capture.
  Extending the Pipeline to deploy the model.
  Making predictions using raw data.
• Session 6 — Monitoring
  An overview of data and concept drift.
  An introduction to Data Quality and Model Quality.
  An introduction to Batch Transform Jobs.
  Extending the Pipeline to compute data quality baselines.
  Extending the Pipeline to compute model quality baselines.
  Scheduling Data and Model Monitoring Jobs.

Here is the list of pre-recorded lectures. Lectures marked with (*) are currently in production and will be released in the coming weeks:

• Lesson 1.1 - Introduction to the Program
  Prerequisites and expectations. Relevance of this program and how it fits in real-world Machine Learning systems. Structure of the program and how best to take advantage of it.
• Lesson 1.2 - What Makes Production Machine Learning Different?
  Differences between Machine Learning in a Research Setting versus Production Machine Learning. Data, Priorities, Efficiency, Interpretability, and Fairness.
• Lesson 1.3 - The Machine Learning Process
  An introduction to a process for building Machine Learning systems. Misconceptions that hold teams back.
• Lesson 1.4 - An Example Machine Learning System
  Breaking down a system to automatically quote the price of luxury watches. How a project flows through the Machine Learning Process.
• (*) Lesson 2.1 - Mapping Machine Learning To Business Objectives
• (*) Lesson 2.2 - Framing Machine Learning Problems
• (*) Lesson 2.3 - Labeling

An important note about joining the program: You pay once to join and get lifetime access to every class, session, lesson, and resource in the community. No recurrent payments. Ever.

What students are saying

• (...) buying access to the community and courses is one of my best purchases. The value-for-money ratio is fantastic, and some of the additional work you have done on top of the SageMaker course is great. I was not expecting that much value other than a SageMaker course, and you have gone above and beyond that, so thank you very much! — A student who asked to remain anonymous.