Turn advanced ML papers into scalable and attractive applications
Code ML models in Python
Learn to read ML papers and implement their solutions.
Deploy ML models with C++
Get high performance and reliability in production.
Build web/mobile applications
Understand how to interact with your deployed ML models.
Guided tutorials
No need to worry about taking lots of notes along the way.
Video content
Watch YouTube videos to digest the content more easily.
Real-world projects
Practice what you learn.
Educator
Aarón Espasandín,
Machine Learning Engineer
With a BSc in Computer Science from the UC3M and over eight years of programming experience, he created this platform for programmers who want to learn how to develop and deploy machine learning models in a practical way, rather than just making API calls to LLMs.
Core Skills for ML Engineers
Develop
Code the ML models from papers
Learn the fundamentals of Deep Learning to read and implement advanced ML models from papers in PyTorch.
Train
Create ML pipelines
Perform exploratory data analysis, create a training pipeline using PyTorch, and utilize CUDA to accelerate the process.
Deploy
Edge and cloud deployment
Use skills such as C++, ONNX Runtime, and TorchServe to deploy models at the edge and in the cloud with AWS and Cloudflare.
Integrate
Use ML in mobile and web applications
Find out how to run inference on the deployed ML models using a web or a mobile application.
Use software engineering
Design efficient and scalable ML architectures
Design and implement different software architectures for using ML models in mobile and web applications.
Apply MLOps
Apply DevOps to ML
Understand the full lifecycle of ML projects to monitor and continuously train your ML models.