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

   • Why most projects fail and an 8-question checklist to make sure yours doesn't.
   • A counterintuitive technique to simplify any problem before you start building.
   • How to build a solution without writing a single line of machine learning code.
   • Better data beats better models and what this means in practice.
   • How to know whether you need more data.
   • How to label data at scale without labeling everything by hand.
   • Transforming messy, raw data into features your model can actually use.

2. Session 2 - How To Choose and Evaluate a Working Model

   • Why 90% accurate models are often useless.
   • How to choose the simplest model that will actually work.
   • Using existing models vs building from scratch vs fine-tuning.
   • The problem with "state-of-the-art" models.
   • How single metrics can hide severe failures in your model.
   • Testing strategies to catch problems before your users do.
   • Using large language models to evaluate complex outputs.
   • Implementing guardrails that keep your system safe.

3. Session 3 - How To Get Models Ready For Production

   • Why improving your data often beats improving your model.
   • A few ways to fix it useless models.
   • Outliers and edge cases, and how they can break your system.
   • How your model's predictions can corrupt its own training data.
   • The fastest way to improve any AI application without touching the model.
   • How to turn your system from a black box into a glass box.
   • Why you can't debug without versioning, and how to set it up early.

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

   • How to pick the right tradeoff between fast, cheap, and accurate predictions.
   • Computing predictions in real time vs. pre-compute them in advance.
   • How to use two-phase predictions.
   • Using a gateway to decouple your application from your models.
   • How caching can cut costs dramatically, and when it backfires.
   • How to implement human-in-the-loop workflows.
   • How to compress your model without destroying its accuracy.

5. Session 5 - How To Monitor And Retrain Your Models (Drift Is Awful)

   • What's data drift and why it matters
   • What to monitor in production applications.
   • How to test new models on real users without breaking everything.
   • When to retrain from scratch vs. update what you already have.
   • Why models forget what they learned and how to prevent it.

6. Session 6 - How To Build Agentic Systems

   • How to give your model access to knowledge it wasn't trained on.
   • The difference between simple agentic workflows and fully autonomous agents.
   • Using MCP to standardize how agents connect to external tools and data.
   • How multiple agents can discover each other and work together.

7. Code walkthroughs

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

   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 developers, data scientists, data engineers, data analysts, technical managers, and anyone who wants to use Artificial Intelligence and Machine Learning 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 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're not afraid to ask questions, share what you're working on, and help others grow.
• 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 three 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 21: February 2 - February 19, 2026. 10:00 AM EST
• Cohort 22: May 4 - May 21, 2026. 2:00 PM EDT

You don't have to wait for a specific cohort to join the program. You have lifetime access, so you can join any time and lock in the current price. The sooner you join, the cheaper it will be.

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 introductory class.

While we'll discuss many fundamental ideas behind Artificial Intelligence and 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.