The Real-World ML Tutorial
4h 3m 44s
English
Paid
Course description
Hello! I am Pau, a machine learning engineer with many years of experience in developing real ML products. Would you like to design, develop, and implement your own ML product?
This course will show you how to create fully functional ML solutions from concept to production, helping startups and large companies solve business challenges.
Read more about the course
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 full-fledged ML solution, not just a simple prototype.
- Data preparation
- Learn how to transform raw data into ready features and target variables.
- Create a reliable data pipeline in Python: collection, validation, transformation, and generation of training data.
- Model prototyping
- Learn to quickly create and improve basic models.
- Step by step, enhance them using feature engineering and boosting.
- Master hyperparameter optimization to get the most out of the data.
- Deploying and monitoring the model
- Turn a prototype into a working batch-scoring system using Feature Store and CI/CD.
- Create a dashboard to display "live" forecasts.
- Set up a system for monitoring the quality and stability of the model.
Who is this course for?
- For those who can already prepare data and train models in a notebook but don't know how to turn them into a working service.
- For specialists who want to learn how to design, implement, and deploy a real ML solution from start to finish.
- For those who aspire to get a job as an ML engineer and want to master the practice of building complete ML systems.
What will you get:
- 3 hours of video lectures and presentations, regularly updated.
- Full source code in a GitHub repository.
What will you build:
In the course, you will create a complete ML service that forecasts taxi demand in New York. The methods and tools you will master (pipelines, MLOps, monitoring, etc.) are applicable in any industry.
Forget about "toy" projects. Here you will learn to build truly working ML systems, bringing value to the business.
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/ #1: Welcome
All Course Lessons (45)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 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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|---|---|
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