Let’s Rust

12h 40m 9s
English
Paid

Course description

A live, practical course where we will together (without lengthy theory) create a working ML product in Rust: train a model, package it, and set up a REST API for predictions, and then deploy the service in Kubernetes. The course is truly "live": the instructor does not position themselves as a die-hard Rust guru — we proceed step by step, relying on dialogue with a code assistant and focusing on the result.

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What you will build

  • A training pipeline for the model: loading CSV, feature engineering in Polars, training, and saving the artifact.
  • A simple model "registry" based on Amazon S3 (uploading the artifact).
  • A REST API in Rust for online predictions: loading the model, processing the request, and providing the response.
  • Demonstration of deployment in Kubernetes (how to build, package, and launch the service).

Why Rust (and what you will have to come to terms with)

  • Pros: high speed, memory and hardware efficiency - lower production costs, strict typing, and safety.
  • Cons: syntax is more complex than Python; you'll need to get used to compilation and tools. We don't "become experts in 4 evenings" - we build a working thing and along the way master key concepts (struct, trait, crate, working with packages, etc.).

Result

By the end of the course, you will have a working service in Rust for ML model inference, an understanding of key tools in the ecosystem, and "muscle memory" for assembling production-ready components. As a bonus, you will also become more precise in Python: a new language broadens your perspective and disciplines your architecture.

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#1: Introduction to the course

All Course Lessons (64)

#Lesson TitleDurationAccess
1
Introduction to the course Demo
08:20
2
What is the goal of this course?
08:52
3
Why Rust?
15:17
4
Our plan
10:43
5
Installing the tools: rustc, cargo, rust-analyzer
16:28
6
How to generate and run the binaries
21:04
7
Steps to train an ML model in Rust
10:00
8
How to download the CSV file - Part 1
26:56
9
How to handle errors and Results with anyhow and ?
20:22
10
Generic types, traits and trait bounds in function definitions
23:10
11
How to download the CSV file - Part 2
10:37
12
Wrap up
03:17
13
Goals for today
05:04
14
Load the CSV file into memory with Polars Rust
18:15
15
Extract functions to lib.rs
05:11
16
Splitting the data into training and test - Part 1
25:31
17
Splitting the data into training and test - Part 2
18:33
18
Questions and answers
04:06
19
Split datasets into features and targets
12:21
20
Transforming Polars DataFrame to DMatrix for XGBoost training
24:43
21
Training the XGBoost model - Part 1
20:48
22
Questions and answers
02:53
23
Training the XGBoost model - Part 2
12:04
24
Pushing the model artifact to AWS S3 bucket
18:41
25
How to set up your AWS credentials to talk to your S3 bucket
01:48
26
Wrap up
01:49
27
Goals for today
03:35
28
Tip -> devcontainers
05:08
29
Add 2 entry points, one for training, one for REST API
05:55
30
Questions and answers
02:35
31
Building a minimal API with health endpoint - Part 1
19:55
32
Building a minimal API with health endpoint - Part 2
10:25
33
Adding a predict endpoint to our REST API
22:20
34
Tip -> How to break a large problem into smaller (easier) ones
02:34
35
Download model artifact from AWS S3 bucket
23:37
36
Refactorings -> Break our lib.rs into separate files
17:27
37
Adding CLI parameters for training using Clap
11:24
38
Loading the model from S3 into memory
07:39
39
Adding model to the AppState so we can use it in our predict functions - Part 1
21:03
40
Adding model to the AppState so we can use it in our predict functions - Part 2
07:46
41
Wrap up
02:32
42
Plan for today
02:17
43
Transform input payload to dmatrix for XGBoost
21:04
44
Formatting the response with the prediction
15:42
45
Extracting the port number as input argument
07:26
46
Two challenges for you
11:22
47
Dockerizing the API - Part 1
20:40
48
Trick -> How to solve Docker no space left problems
06:49
49
Dockerizing the API - Part 2
23:00
50
Dockerizing the API - Part 3
22:35
51
Dockerizing the API - Part 4
21:47
52
Dockerizing the API - Part 5
07:43
53
Dockerizing the API - Part 6
01:51
54
Wrap up
02:37
55
00 - Intro
02:49
56
01 - Bootstrap the project
04:27
57
02 - Hello world with a /health endpoint
21:43
58
03 - A bit of refactoring
02:58
59
04 - Add a /predictions endpoint
08:39
60
05 - Break down project in several files
08:28
61
06 - Start Postgres DB with Docker
07:52
62
07 - Fetch data from Postgres
20:28
63
08 - Loading environment variables
02:07
64
09 - Add GET parameter to the /predictions endpoint and compile for release
04:57

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