English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 35 Lessons (16h 52m) | 5.02 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.
Table of Contents
1 Getting Started
2 Preparing Your Local Environment
3 Introduction to Metaflow
4 Training the Model
5 The Training Pipeline
6 Building a Custom Inference Process
7 Deploying The Model
8 The Endpoint Pipeline
9 Monitoring The Model
10 The Monitoring Pipeline
11 Production Pipelines in Amazon Web Services
12 Deploying the Model to SageMaker
13 The Deployment Pipeline
14 Monitoring the SageMaker Endpoint
15 Running Pipelines Remotely
16 Introduction and Initial Setup
17 Exploratory Data Analysis
18 Splitting and Transforming the Data
19 Training the Model
20 Custom Training Container
21 Tuning the Model
22 Evaluating the Model
23 Registering the Model
24 Conditional Registration
25 Serving the Model
26 Deploying the Model
27 Deploying From the Pipeline
28 Deploying From an Event
29 Building an Inference Pipeline
30 Custom Inference Script
31 Data Quality Baseline
32 Model Quality Baseline
33 Data Monitoring
34 Model Monitoring
35 Shadow Deployments
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