Besbes

Here’s a one-minute demo of the app.Video by the author — A quick demoWith the ever-increasing number of open-source ML models that solve a huge variety of tasks, software applications will gradually become some sort of AI application that integrates pre-trained models, self-trained models, or models accessed through APIs.Given that many SOTA models are large and require powerful hardware and distributed deployment, fitting everything in one machine will not be a practical solution, especially if the application combines…

An end-to-end use-case combining PyTorch Lightning and BentoMLPhoto by SpaceX on UnsplashAs an ML engineer, I often encounter two main challenges when working with deep learning models in production.The first one is the need to rewrite boilerplate code for each project to handle tasks like training loops, data loading, or metric computation. As this work often makes the codebase more complex with unnecessary abstractions, it slows down the iterative process.The second challenge involves the wide range of skills or tools…

Moving from Jupyter notebooks to production is not that difficult after allPhoto by Fran Jacquier on UnsplashIf you’re a data scientist, you probably spend a lot of time developing intricate Jupyter notebooks to perform data analysis, build complex training pipelines, or compute statistics.Jupyter notebooks are great for this and allow us to prototype ideas in no time.But, what happens once you’re done with this work and you’re satisfied with your saved ML models? 🤔This is where you start to think about deploying them to…

Just like unit tests, type hints take developer time but pay off in the long runPhoto by Arian Darvishi on UnsplashPython is a dynamically-typed programming language. This means that the interpreter performs type checking only when the code runs and the variable type is allowed to change over its lifetime.Although Python has always remained dynamically typed and was never intended to change this direction, type hints have been introduced since PEP 484 with the goal of bringing static type checking to the code analysis.In…

I’ve never used spaCy beyond simple named entity recognition tasks. Boy was I wrong.Photo by Lucas Kapla on UnsplashWhile I was working on an NLP project lately, I came to revisit the spaCy library and try out many of its core functionalities to perform low-level linguistic tasks.If you’ve never heard about it, spaCy is a modern natural language processing library that handles +66 languages, provides state-of-the-art speed, and has various components for named entity recognition tasks, part-of-speech tagging, entity…

A new widget to better structure the layout of your appsPhoto by David Bruno Silva on UnsplashAs a data scientist, I use Streamlit a lot to build interactive web applications and showcase demos and prototypes.Streamlit is extremely easy to use, it has a very intuitive syntax and gets you up to speed for prototyping, in no time. You don’t need to know HTML or JavaScript now to build great web apps.Streamlit provides a neat API to craft and design interactive apps in Python.This API is constantly evolving with the help of a…

Garbage in, garbage out: good-quality data is the fuel to robust ML enginesPhoto by Patrick Tomasso on UnsplashOne of my clients recently asked me to build an NLP model for a very specific task in a very technical domain.This was challenging since I didn’t know anything about the subject matter and no training data was available.As a data scientist, I first tried researching open-source datasets, pre-trained models, and scientific articles but I finally came to the conclusion that nothing really matched what I was aiming…