🔭 Improving Your ML Datasets With Galileo

This post was written in collaboration with our sponsors from Galileo. Ben Epstein, Jonathan Gomes Selman, Nidhi Vyas At Galileo, we strongly believe that the key to unlocking robust models is clean, well formed datasets. Although data quality issues are... View article

Why use GPUs instead of CPUs?

—by Jonathan Cosme, AI/ML Solutions Architect at Run:ai Today, we’re going to talk about why you should use GPUs for your end-to-end data science workflows – not just for model training and inference, but also for ETL jobs.  First of... View article

A Quick Guide to Low-Resource NLP

This article is written by Victoria Firsanova, a philologist and NLP researcher My passion is to challenge myself. Although my domain in Natural Language Processing is Conversational AI, this year I decided to try myself in low-resource machine translation. While... View article

Putting together a continuous ML stack

Author: Itay Ben Haim This post is a collaboration with our partners Superwise. The original post can be found here. Due to the increased usage of ML-based products within organizations, a new CI/CD-like paradigm is on the rise. On top... View article

Coffee Sessions Takeaways: A Journey in Scaling ML

There’s a time and place for everything. Seasoned ML engineers like Gabriel Straub may have just mastered that time and place for machine learning. As the current Chief Data Officer for Ocado, he brings over 10 years of experience leading... View article

Building a Machine Learning Platform

This blog is written by John Roberts. He summarizes the MLOps coffee chat session with Orr Shilon at Lemonade. Machine learning has centered on building accurate models which initiated the development of frameworks like TensorFlow, PyTorch and Scikit-Learn. The job... View article