1. Day 1 - How To Start (Almost) Any Project

   In this session, you'll learn how to pitch, sell, and structure a Machine Learning project. You'll learn how to approach new projects, frame complex problems, ask the right questions during discovery, deal with selection bias, and approach data collection and labeling using active learning and weak supervision.

2. Day 2 - How To Build A Model (That Works)

   In this session, you'll explore data cleaning and feature engineering, learn how to preprocess data through vectorization, normalization, and imputation, and discover strategies for selecting the best model for any given problem. You'll learn how to iteratively build an end-to-end training pipeline, and get an introduction to distributed training to scale model training using data and model parallelism.

3. Day 3 - How To Ensure Models Aren't Lying to Us

   In this session, you'll learn about different evaluation strategies like cross-validation, backtesting, invariance, and behavioral testing. You'll learn how to frame evaluation metrics in the context of business goals and ensure models work in real-world scenarios. Aditionally, you'll learn to prevent data leakages, test for fairness, perform error analysis, and work with imbalanced data.

4. Day 4 - How To Serve Model Predictions (In A Clever Way)

   In this session, you'll learn how to version and deploy models and deal with deployment tradeoffs. You'll learn different strategies for serving predictions, using human-in-the-loop workflows, and using cost-sensitivity to improve model performance. Additionally, you'll learn about pruning, quantization, and knowledge distillation to compress models and optimize their performance in real-world applications.

5. Day 5 - How To Monitor A Model (Drift Is Awful)

   In this session, you'll learn how to handle edge cases and outliers, address feedback loops, and detect distribution shifts like covariate shift, label shift, and concept drift. You'll learn how to use adversarial validation and explore strategies for monitoring models in production. Finally, you'll learn different techniques to build resilient models that adapt to distribution shifts.

6. Day 6 - How To Build Continual Learning Systems

   In this session, you'll learn how to automate the end-to-end process of building, deploying, and maintaining a model in production. You'll learn how to implement incremental training, avoid catastrophic forgetting, and use different retraining strategies to keep a model running. Additionally, you'll learn how to test models in production using A/B testing, shadow deployments, canary releases, and interleaving experiments.

7. Code walkthroughs

   You'll get access to an end-to-end, production-ready template system for training, evaluating, deploying, and monitoring machine learning models.

   The codebase comes with extensive documentation to help you understand how the code works and how you could change it to accommodate your needs.

8. Office Hours

   Every week, we'll meet during office hours to answer any open questions, discuss relevant topics, and help you with any challenges you may be facing. This is also a great opportunity to connect with other students in your cohort, share insights, and talk about anything you are building or are passionate about.

Who Is This Program For?

This is hands-on program for people willing to put in the work to build skills with real-world impact.

This program is for software engineers, data scientists, data engineers, data analysts, machine learning engineers, technical managers, and anyone who wants to use Machine Learning and Artificial Intelligence to solve real-world problems.

Here are the prerequisites to succeed in the program:

• You are not afraid of writing code. We'll use Python, but you'll be fine if you have experience with any other language.
• You are familiar with basic machine learning terminology, like model training, evaluation metrics, and model inference. You don't have to be an expert, but this won't be an introductory class.
• You have a basic understanding of cloud services and how to build and deploy a simple API. Familiarity with Docker and containerization is not required but a helpful skill to have.
• You are ready to put in the work and commit the time necessary to succeed.

Upcoming Cohorts

Each iteration of the cohort consists of six live sessions plus office hours over three weeks.

Live sessions take place every Monday and Thursday. Office hours take place on Wednesdays. Every session is recorded. You can attend live or watch the recorded version later.

Here are the upcoming cohorts:

• Cohort 18: May 5 - May 22, 2025. 2:00 PM EDT

Do not wait for a specific cohort to join the program. You have lifetime access, so you can join any time to lock in the current price.

Frequently Asked Questions

If you can't find the answer to your question, please reach out and I'll be happy to help.

How long will it take to complete the program?

Set aside a minimum of 4 hours every week during the three weeks of the program to attend the live sessions or watch the recordings. You'll need an additional 2 - 4 hours if you plan to go through the codebase.

What happens if I can't attend a live session?

Every live session is recorded. If you can't attend a live session, you can catch up asynchronously later using the recording.

I'm a complete beginner. Will this program be helpful for me?

This program is not an introduction to machine learning.

While we'll discuss many fundamental ideas behind machine learning, beginners will find the sessions go much faster than what's optimal for them.

What does "lifetime access" mean?

You only pay once to join the program and get immediate access to every past, present, and future cohort.

Every new iteration of the program is better than the ones before. Many students take classes once and then join a later cohort to benefit from the updates.

The lifetime access removes any pressure from having to complete the program when life gets in the way.