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

   In this session, you'll learn how to frame complex problems and run an effective discovery phase. You'll learn to develop a robust data strategy and labeling plan, build an initial prototype to test your assumptions, and explore data cleaning and feature engineering techniques to prepare your data for modeling.

2. Day 2 - How To Build Better Software (That Works)

   In this session, you'll explore how to establish baselines and create an initial evaluation protocol for your application. You'll learn different strategies for picking the best model and the importance of model versioning and tracing. Finally, you'll learn about Retrieval-Augmented Generation (RAG) and how to use it in production applications.

3. Day 3 - How To Build Software You Can Trust

   In this session, you'll learn how to ensure data quality and integrity throughout your workflows. You'll explore different techniques to deal with class imbalances and apply advanced testing strategies like backtesting, invariance and behavioral testing, and using an LLM-as-a-judge to validate your applications. You'll learn how to perform error analysis and how to implement input and output guardrails to ensure your application works as expected.

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

   In this session, you'll explore how to deploy models while dealing with trade-offs and operational considerations. You'll examine different strategies for serving predictions, including human-in-the-loop and cost-sensitive workflows. You'll learn how to use model routers and gateways and how to use model compression techniques like 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 use caching to optimize the performance of your application. You'll learn to handle edge cases, outliers, and positive feedback loops, and detect and understand distribution shifts like covariate shift, label shift, and concept drift. You'll explore practical monitoring strategies for models in production. You'll learn how to implement retraining strategies 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 use the Model Context Protocol (MCP) and build powerful agentic workflows. You'll explore how to test models in production using A/B testing, canary releases, and shadow deployments. Finally, you'll learn how to build continual learning systems that learn and improve over time.

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 19: August 4 - August 21, 2025. 10:00 AM EDT
• Cohort 20: November 3 - November 20, 2025. 2:00 PM EST

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 Mearning, 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.