The Real-World ML Tutorial

4h 3m 44s
English
Paid

Hello! I am Pau, a machine learning engineer with extensive experience in developing real ML products. Are you ready to design, develop, and implement your own ML product?

This course will guide you in creating fully functional ML solutions from concept to production, enabling startups and large companies to tackle business challenges effectively.

Course Highlights

What awaits you inside:

  • From Business to ML
    • Learn to translate a business problem into an ML task.
    • Master four key steps: from raw data to a comprehensive ML solution, beyond just a simple prototype.
  • Data Preparation
    • Transform raw data into ready features and target variables.
    • Create a reliable data pipeline in Python, covering collection, validation, transformation, and generation of training data.
  • Model Prototyping
    • Quickly create and improve basic models.
    • Enhance them step-by-step using feature engineering and boosting.
    • Master hyperparameter optimization to maximize data utility.
  • Deploying and Monitoring the Model
    • Turn a prototype into a functioning batch-scoring system using Feature Store and CI/CD.
    • Create a dashboard to display live forecasts.
    • Set up a system to monitor the quality and stability of the model.

Target Audience

Who is this course for?

  • Individuals who can prepare data and train models in a notebook but don't know how to turn them into a working service.
  • Specialists aiming to learn how to design, implement, and deploy a real ML solution from start to finish.
  • Aspiring ML engineers who want to master the practice of building complete ML systems.

Course Benefits

What will you get:

  • 3 hours of video lectures and presentations, regularly updated.
  • Access to the full source code in a GitHub repository.

Course Project

What will you build:

Throughout the course, you will develop a complete ML service that forecasts taxi demand in New York. The methods and tools you will master, such as pipelines, MLOps, and monitoring, are applicable across various industries.

Move beyond "toy" projects and learn to build truly effective ML systems that deliver business value.

About the Author: Pau Labarta Bajo

Pau Labarta Bajo thumbnail
I am a machine learning engineer with many years of experience creating real ML products. Do you want to design, develop, and implement your own solution? I explain how to build complete ML products from idea to production and help leading startups and large companies solve business challenges using AI.

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#1: Welcome
All Course Lessons (45)
#Lesson TitleDurationAccess
1
Welcome Demo
00:50
2
Development tools: VSCode, Python Poetry and Git/GitHub
00:57
3
Install VSCode and Python Poetry in your local machine
02:18
4
Create the Project Structure
02:19
5
Create local git repository
02:05
6
Create remote GitHub repository and connect it to the local one
01:55
7
Let's understand the business problem
07:34
8
3 Steps to go from raw data to training data
01:46
9
Our raw data source: the NYC taxi website
01:39
10
Step 1. Fetch raw data and validate it
09:22
11
Step 2. Transform raw validated data into time-series data
06:13
12
Plot the time-series data
02:44
13
Step 3. Transform time-series data into training data
09:38
14
Steps 1, 2 and 3. From raw data to training data + Code re-factoring!
09:56
15
Plot the training data
05:10
16
How do you build a Supervised Machine Learning model?
09:32
17
Split the dataset into training and test datasets
01:47
18
Baseline model 1
06:28
19
Baseline model 2
02:28
20
Baseline model 3
02:40
21
XGBoost model
05:27
22
LightGBM model
03:08
23
LightGBM + Feature engineering
11:40
24
LightGBM + Feature engineering + Hyper-parameter tuning
10:33
25
Batch-scoring ML service with a Feature Store
03:56
26
What is a Feature Store?
01:18
27
Create a Serverless Feature Store with Hopsworks
01:42
28
Backfill Feature Store with Historical Data
08:52
29
Build the Feature Pipeline
05:22
30
Automate the execution of the Feature Pipeline using a GitHub action
04:47
31
Build the Model Training Pipeline
09:15
32
ML Frontend app using Streamlit
02:15
33
Inference functions
04:13
34
Build the Streamlit app - Part 1: inference code
09:43
35
Build the Streamlit app - Part 2: build the UI
05:41
36
Deploy the Streamlit app to Streamlit Cloud
04:54
37
Our plan
02:35
38
Create an inference pipeline to generate and store predictions in the store
07:24
39
The new (and way simpler) frontend Streamlit app - frontend.py
02:56
40
Create a monitoring dashboard with Streamlit
04:07
41
Deploy the monitoring dashboard to Streamlit Cloud
02:33
42
Why model re-training?
03:02
43
Implementation
10:29
44
2024_07_02_Karthikeya
05:11
45
2024-07-09-Karthikeya
25:20
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