Three-Project Series

Deploy ML Models in Production with Ray you own this product

prerequisites
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
includes 3 liveProjects
liveProject $38.99 $59.99 self-paced learning

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.
Get Your Certificate of Completion—Free!
Finish the work in this liveProject to earn a Certificate of Completion. Once you’re done with your build, take a short online test, then contact the liveProject mentor who will review your submissions. Finally, set up a 15-minute video call with the mentor to verify your work.

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

here's what's included

Project 1 Preprocess Data in Parallel

The company you work for, which offers lifestyle products, has enjoyed success for several years, but management has decided it’s not competitive enough. Your task is to identify current market trends and new niche markets for the company’s lifestyle products. Using Python and Ray, you’ll build a web scraper that will load and save multiple web pages concurrently. To preprocess the data, you’ll read each of your locally stored pages, split them into sentences, tokenize each sentence with Hugging Face tokenizers, and store your tokenized documents in a new (pickled) format in your file system. When you’re finished, you’ll have leveraged Ray to preprocess a large amount of data while bypassing Python’s notorious concurrency limitations. The data science team will thank you for helping minimize the data preparation time, and the management team will thank you for helping the company sharpen its competitive edge.

$29.99 $19.49
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Project 2 Serve a Model

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.

$29.99 $19.49
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Project 3 Serve a Compound Model

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.

$29.99 $19.49
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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.

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, asyncio API, encode/decode JSON documents, read/write to files)
  • Beginner NumPy
  • Beginner scikit-learn
TECHNIQUES
  • 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

features

Self-paced
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.
Get Your Certificate of Completion—Free!
Finish the work in this liveProject to earn a Certificate of Completion. Once you’re done with your build, take a short online test, then contact the liveProject mentor who will review your submissions. Finally, set up a 15-minute video call with the mentor to verify your work.
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