Building a Real-Time ML System
Together with LLMs
Join the Best Interactive ML Course On the Internet & Stand Out From the Crowd
Next Cohort: December 2 - December 26, 2024
Get the Early Bird Discount ↓
✔️ Access every future cohort forever. No need to pay again.
✔️ 60+ hours of recorded sessions from the previous 2 cohorts
✔️ Full source code of a crypto price predictor system.
Wondering how real-time ML systems are built?
You’ve come to the right place!In this course, we will build, deploy, and scale a real-time ML predictor for algorithmic trading.
Who is this course for?
Are you an ML engineer, data scientist or a software engineer following hot trends in the industry but missing the hands-on training that would allow you to become a part of the change?I can help you bridge the gap!Build a REAL-TIME Prediction System for Trading with me in a live and interactive 3-weeks course that will change your career forever.It is time to stop following and start BUILDING.To succeed, you should:
Have experience writing code. We will use Python but if you know a different programming language, you can follow easily.
You are familiar with the Machine Learning fundamentals and you’ve trained at least one ML model before.
You are not afraid of hard work. (That will have a huge pay off, I promise.)
Why should you join?
Because when you join you get life-time access to:
Full source code of many real-time ML systems: so far a crypto price predictor, and a taxi trip duration à la Uber.
36 hours of live, interactive coding sessions.
We meet 3 times a week, and we code for 3 hours. We do this for 4 weeks, hence 36 hours.
Direct communication with your instructor (that’s me, by the way).
A community of similar-minded ML builders eager to learn new things and solve real-world problems.
A list of project ideas you will be able to implement after finishing the course & feedback on your final project.
Lifetime access to all future cohorts' sessions. You can participate in as many iterations as you want.
The possibility to work with top companies. If you do a good job I will be more than happy to give visibility to your work on my social media accounts and recommend you to my clients.
The best part? You pay ONCE and get access to all live and recorded future sessions. That means you don’t have to stress if you skip anything in this cohort, you can join the future ones whenever you want :-)Forget about theory and passive learning, it is time to roll up your sleeves and become a part of an exciting and challenging project that will turn your abstract theoretical knowledge to ready-to-apply PRACTICE.
What will you learn?
This is what makes this program unique:
You’ll learn to design, build, deploy and scale microservice architectures that use Real-Time Machine Learning to continuously generate predictions.
You will learn to design any ML system using the universal Feature-Training-Inference Pipeline design.
You will learn how to build real-time ML services in Python, using best-of-breed Serverless tools, including a Kafka streaming data platform, a Feature Store, an Experiment Tracker, a Model Registry, and a Compute Platform à la Kubernetes.
Ready for a serious knowledge upgrade?
Join the program TODAY and get a life-time access to building the hottest real-time ML project out there!
Early bird pricing until 1st of June
Still have doubts? Reach out on social media and I'll be happy to help
$700
$420
🚨 40% discount ending soon
You pay once and get lifetime access to all future sessions. No restrictions.
Build a real-time ML system from scratch over 4 weeks.
Access to the full source code of an ML predictor for algorithmic trading.
Learn to integrate Large Language Models in an end-2-end ML system.
36+ hours of live, interactive coding sessions.
Office hours sessions to solve any doubts you may have.
Direct communication with your instructor (that’s me, by the way).
A community of similar-minded ML builders eager to learn new things and solve real-world problems.
A list of project ideas you will be able to implement after finishing the course & feedback on your final project.
Lifetime access to all future cohorts' sessions. You can participate in as many iterations as you want.
The possibility to work with top companies. If you do a good job I will be more than happy to give visibility to your work on my social media accounts and recommend you to my clients.
Current cohort
All coding sessions and office hours start at 11:00 AM Central European Time.All coding sessions and office hours are recorded and uploaded If you cannot make it to an office hour, but would like to ask something, please ask on the Discord Community, and I will answer during the session.
Monday → Live session, 3 hours
Tuesday → Individual work
Wednesday → Office hours + coding (3 hours)
Thursday → Live session, 3 hours
Friday → Individual work
Program syllabus
Session 1 - How to build a real-time ML system
Problem framing
System architecture
The Feature-Training-Inference pipeline design
How to iteratively build modular ML systems
Open-source development tools and Serverless Infrastructure
Feature pipeline components
Docker fundamentals
Let's develop, containerize and run our first microservice using Python and Docker.
Makefile to streamline the development workflow
Automatic code linting and formatting
Session 2 - The Feature pipeline
Real-time data ingestion from Websocket APIs
Stateful real-time data transformations in Python
Reading and writing data to Kafka in Python
How to write modular Python code, that is easy to debug and test.
What is a Feature Store and why do we need one?
Backfilling of historical data
What is offline-online skew and how to avoid it
Kafka topics, partitions and consumer groups
Horizontal scaling of data transformations with Docker and Kafka.
Session 3 - The Training pipeline
Iterative development of ML models.
Training and evaluation of time-series forecasts
Model-dependent transformations
Feature engineering using trading technical indicators
Target metric parametrisation
Integration of experiment tracking
What is a model registry and how to integrate it to the training pipeline
Dockerization for reproducible runs
Session 4 - The Inference pipeline
Real-time ML model prediction in Python
How to build a streaming microservice for live predictions
Visualization of prediction in Streamlit
Connecting the inference pipeline to the model registry
Session 5 - Deployment and monitoring
Deployment of microservices à la Kubernetes
Feature latency monitoring using Prometheus and Grafana
Feature quality validation
Model error monitoring
Model re-training
Session 6 - System improvements
This session is open-ended at the moment. Based on your feedback from the previous session I will decide what we cover here.
Feature engineering hyperparameter optimization
Horizontal scaling of feature engineering services
Real-time visualization of features with Streamlit
Open-ended
Hey! I'm Pau.
I am a Mathematician turned Machine Learning Engineer, turned Machine Learning educator.I love solving problems, so much so that I participated in the International Mathematical Olympiad. And ML is all about solving problems.I started my career as a Quantitative Analyst for Erste Bank more than 10 years ago and then worked as a Data Scientist in a mobile gaming company Nordeus (now Take-Two Interactive).There, I had my first real-world ML project and decided this is something I want to do.Since then, I’ve worked as a freelancer at Toptal on projects involving self-driving cars, finance, delivery aps, time-series prediction for online retail, health insurance providers, and more.Two years ago, I started sharing what I know with my community on Twitter/X and LinkedIn. My first course The Real-World Machine Learning Tutorial has more than 500 happy students and I keep sharing free content on my social media.Over the years, I’ve realized that the only way to learn is to actually DO it and in this course I will be sharing everything I know about real-time ML projects with you.This course gives you practical, ready-to-use knowledge that will change your career for the better.I can’t wait to meet you. See you at the course!
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