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

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:

• Cohort #6: Sep 18 - Sep 29. 10 am EST. (Monday, Wednesday, and Friday)
• Cohort #7: Oct 16 - Oct 27. 2 pm EST. (Monday, Wednesday, and Friday)

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

Session 1 - Building a Pipeline

This session will introduce the program and start building the production pipeline. We’ll cover the following topics:

1. Introduction to the program.
2. An application about Penguins.
3. Introduction to Machine Learning Pipelines.
4. Designing a production pipeline.
5. SageMaker Processing Jobs and the Processing Step.
6. Transforming and splitting the Penguins dataset.
7. Configuration and caching of pipelines.

Session 2 - Training and Tuning

This session will extend the pipeline with a step for training a model. We’ll cover the following topics:

1. Training and tuning in production systems.
2. SageMaker Training Jobs and the Training Step.
3. SageMaker Hyperparameter Tuning Jobs and the Tuning Step.
4. A multi-class classification network to predict species of penguins.
5. Implicit and explicit dependencies between pipeline steps.

Session 3 - Evaluation and Registration

This session will extend the pipeline with a step for evaluating the model and another for registering it in the Model Registry. We’ll cover the following topics:

1. Model versioning in production systems.
2. Evaluating the Penguins model.
3. Introduction to the Model Registry.
4. The SageMaker Model Step.
5. The SageMaker Condition Step and Fail Step.

Session 4 - Deploying the Model

This session will extend the pipeline with a step for deploying the model to an endpoint. We’ll cover the following topics:

1. Deploying directly from the Model Registry.
2. Custom inference code.
3. Introduction to model repacking in SageMaker.
4. Automatically capturing live traffic.
5. The SageMaker Lambda Step.
6. Extending the Pipeline to deploy the model.

Session 5 - Data Monitoring

This session extends the pipeline to computing a data baseline and sets up a Data Monitoring Job to detect anomalies with live traffic data.

1. Identifying data drift from first principles.
2. Computing a data baseline to detect data drift.
3. The SageMaker QualityCheck Step.
4. Setting up a Data Monitoring Schedule.

Session 6 - Model Monitoring

This session extends the pipeline to computing a performance baseline and sets up a Model Monitoring Job to detect any drift or anomalies with the model predictions.

We will cover the following topics:

1. Identifying model drift from first principles.
2. Computing a performance baseline to detect model drift.
3. SageMaker Batch Transform Jobs and the Transform Step.
4. Generating ground-truth data.
5. Computing performance metrics.
6. Setting up a Model Monitoring Schedule.

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.