Data Engineering

MLOps is 98% Data Engineering.

MLOps emerged as a new category of tools for managing data infrastructure, specifically for ML use cases with the main assumption being that ML has unique needs. After a few years and with the hype gone, it has become apparent that MLOps overlap more with Data Engineering than most people believed. Let’s see why and what that means for the MLOps ecosystem.... View article


The emergence of the full-stack ML engineer

This blog was written by Prassanna Ganesh Ravishankar, Senior Machine Learning Software Engineer at Papercup. A bit of history “In the beginning was the code” (nice TED talk here). Way back in the 1980s, the client-server programming paradigm came into being. This changed... View article

MLOps recipes

The MLOps Cookbook: how we optimised our Vertex AI Pipelines Environments at VMO2 for scale

The MLOps Platform at VMO2 allows our data scientists and analysts to explore the data, iterate on ML-based solutions and productionise these solutions to make a real impact to enhance the digital experience for our customers. At the heart of this tooling sits Vertex AI Pipelines. In this article, we share how we solved the problem of managing multiple container environments to allow for leaner and faster pipelines, enabling us to scale the number of productionised ML products further. ... View article

How to tame your MLOps Mess with Run:ai

This blog was written in partnership with Run:ai. MLOps – a term that only started to gain steam in 2019 – is big, and only getting bigger. MLOps searches, source: Google Trends. It feels like a new AI / ML... View article

MLOps Trophy

The Coveted MLOp Awards

MLOps Awards 2022 For the first time ever we will be acknowledging the incredible work that has been put into the MLOps ecosystem over the past year. These MLOps Industry Awards are of the highest and utmost honor. Receiving one... View article