Preprocess Data in Parallel you own this product

intermediate Python • intermediate ML and AI • basic NumPy
skills learned
make your Python code concurrent or parallel • build a web scraper • pre-process texts for NLP models with Hugging Face transformers
Delio D'Anna
1 week · 8-10 hours per week · INTERMEDIATE

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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.

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, asyncio API, encode/decode JSON documents, read/write to files)
  • Beginner NumPy
  • Beginner Hugging Face
  • Intermediate ML and AI (classification algorithms, tokenization, word embeddings, dataset scaling)


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