Learn to Build Machine Learning Systems That Don't Suck

32h 6m 40s
English
Paid

A live, interactive course that will teach you from scratch how to design, create, and implement production-ready ML systems - without any fluff or academic drudgery.

This course is for those who want to solve real-world problems using AI and machine learning. Most ML courses are boring, overly academic, and do not teach how to bring a product to a real launch.

But this course is different. It is a practical, honest, and highly applied program that will give you all the skills needed to work with ML systems in production within just a few weeks.

After completing the course, you will:

  • develop, build, and deploy a full end-to-end ML system;
  • receive a proven plan for selling, planning, and delivering world-class projects - based on 30 years of practical experience;
  • and come away with a toolkit that can be immediately applied in your work.

What will you learn?

This is a live and practical course focused on real machine learning. It's nothing like the "online courses" you've taken before:

  • More than 20 hours of live interactive sessions where you will learn to create production-ready ML systems;
  • Best practices for developing, evaluating, deploying, monitoring, and supporting systems in production;
  • Full access to a step-by-step analysis of a complete ML system built from scratch;
  • Training in "build once - deploy anywhere" principles using modern open-source tools;
  • Lifelong access to all future course streams and a private community of thousands of students.

This course will completely change your perspective on machine learning.

No boring theory - only real strategies that work.

About the Author: Santiago Valdarrama

Santiago Valdarrama thumbnail
I am a machine learning engineer with thirty years of experience in developing and scaling enterprise software and machine learning systems. From 2009 to 2023, I had the opportunity to create solutions for companies such as Disney, Boston Dynamics, IBM, Dell, G4S, Anheuser-Busch, HP, and NextEra Energy. These projects taught me what it takes to create reliable and scalable software.

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#1: 001 - Session 1 - How To Start (Almost) Any Project
All Course Lessons (10)
#Lesson TitleDurationAccess
1
001 - Session 1 - How To Start (Almost) Any Project Demo
04:00:28
2
002 - Office Hours 1
02:33:54
3
003 - Session 2 - How To Build A Model (That Works)
03:27:53
4
004 - Session 3 - How To Ensure Models Aren't Lying to Us
04:35:05
5
005 - Office Hours 2
04:53:21
6
006 - Session 4 - How To Serve Model Predictions (In A Clever Way)
03:09:03
7
007 - Session 5 - How To Monitor A Model (Drift Is Awful)
02:54:41
8
008 - Office Hours 3
02:37:01
9
009 - Session 6 - How To Build Continual Learning Systems
03:29:02
10
010 - Code Walkthrough - Introduction
26:12
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