English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 10 Lessons (29h 0m) | 35.27 GB
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.
Update: Cohort 18. The Old one is in Archive too.
Table of Contents
Cohort 16
Lesson 1 – Getting Started
Lesson 2 – Preparing Your Local Environment
Lesson 3 – Introduction to Metaflow
Lesson 4 – Training the Model
Lesson 5 – The Training Pipeline
Lesson 6 – Building a Custom Inference Process
Lesson 7 – Deploying The Model
Lesson 8 – The Endpoint Pipeline
Lesson 9 – Monitoring The Model
Lesson 10 – The Monitoring Pipeline
Lesson 11 – Production Pipelines in Amazon Web Services
Lesson 12 – Deploying the Model to SageMaker
Lesson 13 – The Deployment Pipeline
Lesson 14 – Monitoring the SageMaker Endpoint
Lesson 15 – Running Pipelines Remotely
Session 1 – Introduction and Initial Setup
Session 2 – Exploratory Data Analysis
Session 3 – Splitting and Transforming the Data
Session 4 – Training the Model
Session 5 – Custom Training Container
Session 6 – Tuning the Model
Session 7 – Evaluating the Model
Session 8 – Registering the Model
Session 9 – Conditional Registration
Session 10 – Serving the Model
Session 11 – Deploying the Model
Session 12 – Deploying From the Pipeline
Session 13 – Deploying From an Event
Session 14 – Building an Inference Pipeline
Session 15 – Custom Inference Script
Session 16 – Data Quality Baseline
Session 17 – Model Quality Baseline
Session 18 – Data Monitoring
Session 19 – Model Monitoring
Session 20 – Shadow Deployments
Cohort 17
Session 1 – How To Start (Almost) Any Project
Office Hours 1
Session 2 – How To Build A Model (That Works)
Session 3 – How To Ensure Models Aren’t Lying to Us
Office Hours 2
Session 4 – How To Serve Model Predictions (In A Clever Way)
Session 5 – How To Monitor A Model (Drift Is Awful)
Office Hours 3
Session 6 – How To Build Continual Learning Systems
Cohort 18
Session 1 – How To Start (Almost) Any Project
Office Hours 1
Session 2 – How To Build A Model (That Works)
Session 3 – How To Ensure Models Aren’t Lying to Us
Office Hours 2
Session 4 – How To Serve Model Predictions (In A Clever Way)
Session 5 – How To Monitor A Model (Drift Is Awful)
Office Hours 3
Session 6 – How To Build Continual Learning Systems
Code Walkthrough – Introduction
Cohort 19
Session 1 – How To Start (Almost) Any Project
Office Hours 1
Session 2 – How To Build Better Software (That Works)
Session 3 – How To Build Software You Can Trust
Office Hours 2
Session 4 – How To Serve Model Predictions (In A Clever Way)
Session 5 – How To Monitor Your Models (Drift Is Awful)
Office Hours 3
Session 6 – How To Build Continual Learning And Agentic Systems
Code Walkthrough – Introduction
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