Serve a Compound Model you own this product

intermediate Python • intermediate ML and AI • basic NumPy • basic Hugging Face
skills learned
use Ray Serve • use FastAPI • NLP text preprocessing • use Hugging Face transformers to transform text
Delio D'Anna
1 week · 4-6 hours per week · INTERMEDIATE

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The company you work for, which provides a news feed aggregator, is plagued with an influx of hoaxes that are putting its reputation in jeopardy. The data science team has already trained a set of complex natural language processing (NLP) models to distinguish real news from fake news. Your task is to build a service, using Ray, that exposes the endpoint that returns the JSON object categorized as either a hoax or news. Then, you’ll optimize the service for performance and speed, enabling it to perform more parallel operations and use as many GPUs as possible. When you’re finished, you’ll have firsthand experience using some of Ray Serve’s advanced features for serving and optimizing a compound model—and you’ll have kept your company’s reputation safe.

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.


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:

  • Intermediate Python (declare variables and functions, loops, branches, import modules, basic object-oriented programming, pickling)
  • Beginner NumPy
  • Beginner Hugging Face
  • Intermediate ML and AI (classification, word tokenization, word embedding)


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