Learn to Build Machine Learning Systems That Don't Suck

A live, interactive program that'll show you how to design, build, and deploy production-ready systems from scratch — without the fluff.

This program is for builders looking to solve real-world problems using AI/ML.

Most Machine Learning courses are boring, too academic, and never talk about how to ship actual products.

This program is different. This is a practical, no-nonsense, hands-on program that will teach you the skills you need for building production systems in weeks, not months.

You'll walk away from this program having designed, built, and deployed an end-to-end Machine Learning system, plus a proven playbook for selling, planning, and delivering world-class work backed by 30 years of real-world experience.

This is the class I wish I had taken when I started.

What Will You Learn?

This is a live, hands-on program that focuses on real-world Machine Learning.

This program is a world apart from any of those courses you've taken before:

• You'll join 20+ hours of live, interactive sessions where you'll learn how to build production-ready Machine Learning systems.
• You'll discover best practices for building, evaluating, running, monitoring, and maintaining systems in production.
• You'll get hands-on access and a complete walkthrough of an end-to-end Machine Learning system built entirely from scratch.
• You'll learn how to build systems once and deploy them anywhere using state-of-the-art techniques and open-source tools.
• You'll enjoy lifetime access to every future cohort and a private community where you can collaborate with thousands of students like you.

This program will completely change the way you think about Machine Learning. You'll ditch the typical classroom fluff in favor of practical strategies that actually work.

1. Day 1 - How To Start (Almost) Any ProjectIn this session, you'll learn how to pitch, sell, structure, and launch a new Machine Learning project. You'll find out how to frame complex problems in ways that set you up for successful solutions. Then, you'll cover how to run a discovery phase, address selection bias, manage data collection and labeling, and build an initial prototype.
2. Day 2 - How To Build A Model (That Works)In this session, you'll explore data cleaning and feature engineering, and learn how to preprocess data using vectorization, normalization, and imputation. Next, you'll cover strategies for selecting the best model for your problem and discuss how to iteratively build an end-to-end training pipeline. Finally, you'll walk through distributed training so you can scale your models with data and model parallelism.
3. Day 3 - How To Ensure Models Aren't Lying to UsIn this session, you'll explore different evaluation strategies, such as cross-validation, LLM-as-a-judge, backtesting, invariance, and behavioral testing. Next, you'll see how to frame evaluation metrics in the context of business goals, ensuring your models work in real-world scenarios. Finally, you'll learn to prevent data leakages, perform error analysis, and handle imbalanced data.
4. Day 4 - How To Serve Model Predictions (In A Clever Way)In this session, you'll explore how to version and deploy models while dealing with key trade-offs and operational considerations. Next, you'll examine different strategies for serving predictions, including human-in-the-loop and cost-sensitive workflows. Finally, you'll learn about pruning, quantization, knowledge distillation, and Low-Rank Adaptation (LoRA) to compress and optimize models for 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 and understand distribution shifts like covariate shift, label shift, and concept drift. Next, you'll see how to use adversarial validation and explore practical strategies for monitoring models in production. Finally, you'll explore different techniques to build resilient models that adapt to distribution shifts.
6. Day 6 - How To Build Continual Learning SystemsIn this session, you'll learn how to dynamically extend the capabilities of your model using the Model Context Protocol (MCP) and how to automate the entire process of building, deploying, and maintaining a model in production to create systems that learn and improve over time. You'll explore incremental training techniques, how to avoid catastrophic forgetting and different methods for retraining your models. Finally, you'll see how to test models in production using A/B testing, canary releases, shadow deployments, and interleaving experiments.
7. Code walkthroughsYou'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 HoursEvery 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.