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"This is the best machine learning course I've done. Worth every cent."
— Jose Reyes, AI/ML at Cevo Australia
A live, hands-on program to design, build, and deploy production-ready AI/ML systems from scratch — without the fluff.
Most 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 AI/ML 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.
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 AI/ML 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 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 Artificial Intelligence and Machine Learning. You'll ditch the typical classroom fluff in favor of practical strategies that actually work.
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.
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.
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.
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.
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.
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.
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.
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.
"I have learned a ton from Santiago in his class and it was actually what helped inspire me and get into the MLOps work that I'm doing now. Truly one of the most helpful online courses for doing real, full-scale machine learning."
— Brian H. Hough (Software Engineer)
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.
"This is an awesome course! This is my second round and I continue learning. I recommend it with complete confidence." . — Juan Olano (Machine Learning Engineer)
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.
"This is one of the best classes I've ever purchased over the internet. Santiago is a terrific teacher. The ability he has to share knowledge is fantastic. I recommend this course. Worth 10x what he's charging." . — Sal DiStefano
If you can't find the answer to your question, please reach out and I'll be happy to help.
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.
Every live session is recorded. If you can't attend a live session, you can catch up asynchronously later using the recording.
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.
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.
I'm the instructor of the program.
I'm a Machine Learning Engineer with three decades of experience building and scaling enterprise software and AI/ML systems.
I've had the privilege of building systems for companies like Disney, Boston Dynamics, IBM, Dell, G4S, Anheuser-Busch, HP, and NextEra Energy, among others. Across these projects, I learned what it takes to build reliable and scalable software that works.
I started this program in March 2023, and since then, more than 2,000 students have successfully graduated.
I can't wait to see you in class!
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