Literate programming is now a reality through nbdev and the new visual debugger for Jupyter. — Notebooks have always been a tool for incremental development of software ideas. Data scientists use Jupyter to journal their work, explore and experiment with novel algorithms, quickly sketch new approaches and immediately observe the outcomes. However, when the time is ripe, software developers turn to classical IDEs (Integrated Development Environment)…

Turn your Windows machine into a developer workstation with WSL 2. — I used to have an Apple laptop as my daily driver. I could do almost everything there; development, proposal writing, music composition etc. …

Omnipresence, tools to keep you in the zone, and education were the main themes for Project Jupyter in 2022 — Jupyter notebooks are great for software development and documentation. They are widely used in the world of data science and Machine Learning (ML), and it's an ideal tool to use if you want to experiment with new algorithms, analyze and get familiar with your datasets, and create quick sketches of…

How to use Stable Diffusion to create realistic images from a description in natural language, without a GPU. — As a kid, I always admired people who could draw whatever came to mind. I could watch them for hours as they would give shape to seemingly arbitrary lines on paper. Unfortunately, I was not blessed with that gift. Time went by, and today AI can help me materialize the…

How to transfer your machine learning code and its dependencies to your production environment. — Jane works as a Machine Learning (ML) engineer at an accomplished startup. They are about to release the first version of their product, which relies heavily on the performance of the ML algorithm she is working on. …

Maintaining an inference server is distressing but necessary. — This article continues a series of articles tackling the most frightening idea in the world of production ML: putting the damn thing in production. In previous stories, we saw two different approaches to designing a Machine Learning (ML) powered application. …

When to place your model in your database, how to do it, and why — In the previous article, we saw one excellent reason you'd want to use tools like Streamlit and Gradio to deploy fast and deploy many versions of your Machine Learning (ML) application. Keep Your ML Models out of Your Application Servers What it takes to turn a promising ML model into a useful ML-powered producttowardsdatascience.com

What it takes to turn a promising ML model into a useful ML-powered product — Deploying your Machine Learning (ML) models is never a job to take lightly. Sure, some great tools can help you share your models with the world quickly, like Gradio and Streamlit, but if we're talking about anything more than a proof of concept, you have some decisions to make! Gradio…

Software is like entropy: It is difficult to grasp, weighs nothing, and obeys the Second Law of Thermodynamics; i.e., it always increases.— Norman Augustine — Planning out the Machine Learning (ML) project lifecycle is an effective way to organize the decisions and actions that produce a successful outcome. This framework allows you to focus on what is important to get the system up and running and minimize surprises.