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prerequisites
intermediate Python • intermediate ML and AI • basic NumPy
skills learned
use Ray Serve • use FastAPI integration with Ray • deploy a scikit model with Ray Serve
Delio D'Anna
1 week · 4-6 hours per week · INTERMEDIATE
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Step into the role of a machine learning engineer working for a healthcare company that provides software to hospitals. One of your clients, a national health provider, has asked your company to provide software that predicts heart failure in patients. Using scikit-learn, you’ll develop a model that uses linear regression on a public Kaggle dataset containing heart failure data. Using Ray Serve, you’ll first deploy a function that accepts a wide range of parameters, then serve your model and provide functionality for multiple concurrent requests. When you’re done, you’ll have learned to use the Ray framework to serve your model through a webpage and helped your client save lives by using its patients’ parameters to predict imminent heart failure.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Delio D'Anna

Delio D’Anna holds a degree in computing and mathematical science and earned a postgrad diploma in computing. He’s worked in the software industry for over 10 years, mainly on web applications with languages such as PHP, JavaScript, Python, and JavaFirst, as well as Go. He co-authored a book titled The Go Workshop. His focus remains on microservices, scalability, and domain-driven design. In the last 2 years, he’s been working with Python to put trained models in production and automate training pipelines, with a focus on leveraging the increasingly popular Ray framework and tools for ensuring that several models and inference pipelines can be run in parallel.

prerequisites

This liveProject is for data scientists who want to prepare their ML models for deployment to production, as well as software engineers who need to overcome the challenges of ML applications. To begin these liveProjects you’ll need to be familiar with the following:

TOOLS
  • Intermediate Python (declare variables and functions, loops, branches, import modules, basic object-oriented programming, pickling)
  • Beginner NumPy
  • Beginner scikit-learn
TECHNIQUES
  • Intermediate ML and AI (classification algorithms, dataset scaling)
  • Matrix operations

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