ARTICLES

Avoid these common ML monitoring mistakes – your model’s success hangs in the balance.  So you’ve built a machine learning model that works well in the lab. You’ve validated it, gotten the green light from the internal stakeholders, ensured that it met any regulatory...

By: Atindriyo Sanyal, Vikram Chatterji, Nidhi Vyas, Ben Epstein, Nikita Demir, Anthony Corletti Abstract The quality of underlying training data is very crucial for building performantmachine learning models with wider generalizabilty. However, current machinelearning...

Machine learning (ML) becomes effective once models are in production. Organizations, on the other hand, usually underestimate the complexity and challenges of implementing machine learning in production, devoting the majority of their resources to ML development and...

It’s estimated that anywhere from 50-90% of AI models developed never make it past the AI “valley of death” that exists between the lab and production deployment. This tech talk covers how an API-based approach to building and maintaining AI-enabled applications can...

Original article (Mandarin) written by ArtiV developer Chen-en Lu (Popcorny) for the MLOps Taiwan Facebook community. Translated and adapted to English by Dave Flynntl;dr ArtiV is smart version control system for large files, especially suited for use on machine...

What happens when you have two records which are really meant to be one? Most people with a cell phone have encountered this problem with duplicate contacts. The problem of recognizing them is tricky, but when you find the duplicate, you’ve got another problem:...

In the past three years, Zillow invested hundreds of millions of dollars into Zillow Offers, its AI-enabled home-flipping program. The company intended to use ML models to buy up thousands of houses per month, whereupon the homes would be renovated and sold for a...

Machine Learning Infrastructure vs traditional IT Infrastructure 2 years ago I had heard of DevOps and knew some bits and pieces of how AI works, such as: you take data from somewhere, train a model when you’re satisfied you feed it new data and work with the...

In this article, founder and CEO of Galileo Vikram Chatterji discusses the problems with ML data blindspots and introduces ML Data Intelligence that helps an ML team holistically understand and improve the health of the data powering ML across the organization. As a...

I’m not going to list Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, TensorFlow, PyTorch, etc. You probably know about these already. There is nothing wrong with these libraries; they’re already the bare minimum essential for data science using python. And the...