Three-Project Series

Deploy ML Models in Production with Ray you own this product

intermediate Python • intermediate ML and AI • basic NumPy • basic scikit • basic Hugging Face
skills learned
use Ray for processing huge amounts of data • concurrent use of all available CPUs • put a simple model into production • deploy a model in a real-world scenario • put complex models into production
Delio D'Anna
3 weeks · 6-8 hours per week average · INTERMEDIATE

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5, 10 or 20 seats+ for your team - learn more

In this series of liveProjects, you’ll play roles at three different companies, leveraging the Ray framework to prepare and deploy machine learning models to production. Working for a company that wants to find new markets for its lifestyle products, you’ll use Ray to build a web scraper and prepare your scraped data for training an ML model.

As a machine learning engineer working for a healthcare company that provides software to hospitals, you’ll use the Ray framework to serve a model through a webpage and help your client save lives by using its patients’ parameters to predict imminent heart failure.

Finally, you’ll help protect the reputation of a company that provides a news feed aggregator by deploying and optimizing a complex, compound NLP model that distinguishes fake news from real news. When you’re finished with these liveProjects, you’ll have learned Ray basics, as well as more advanced features for preprocessing large volumes of data, making concurrent use of all CPUs, and deploying ML models to production.

These projects are designed for learning purposes and are not complete, production-ready applications or solutions.

I think this is quite important to know. Ray is a really useful library and is helpful for parallel training.

Ayush Bihani, Research, Tata Consultancy Services

book resources

When you start each of the projects in this series, you'll get full access to the following book for 90 days.

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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, asyncio API, encode/decode JSON documents, read/write to files)
  • Beginner NumPy
  • Beginner scikit-learn
  • Intermediate ML and AI (classification algorithms, tokenization, dataset scaling)
  • Matrix operations

you will learn

In this liveProject series, you’ll learn to use the Ray framework to prepare and deploy machine learning models to production.

  • The basics of the Ray framework
  • How to make your Python code concurrent or parallel
  • How to perform pre-processing of texts for NLP models with Hugging Face transformers
  • How to use Ray Serve to deploy any function in a scalable way
  • How to use the FastAPI integration with Ray
  • How to accept complex parameters for a function exposed through Ray Serve
  • NLP text preprocessing
  • Hugging Face transformers
  • NLTK tokenizers
  • Word embeddings


You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.