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
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
This blog was written by Stefano Bosisio What do we need today? Firstly, let’s think of the design of the main SDK protocol. The aim today is to allow data scientists to: Thus, we can think of implementing the two... View article
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
To be successful with machine learning, you need to do more than just monitor your models at prediction time. You also need to monitor your features and prevent a “garbage in, garbage out” situation. However, it’s extremely hard to detect... View article
This post was written by Ankit Aggarwal and Vinay Anantharaman at Aurora Innovation. Machine learning is the backbone of autonomous vehicle development. Learn how Aurora’s engineering team designed and implemented a centralized ML orchestration layer that allows it to iterate... View article
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
Author: Arseny Kravchenko At Ntropy, machine learning models are the core of our tech and product, and we spend a significant share of our engineering efforts improving them. Aiming for quicker iterations, we are constantly looking for ways to improve the... View article
This post was originally written by Jake Noble for the tecton.ai blog. Have you ever wondered how TikTok can recommend videos to you that were uploaded minutes ago? Or how YouTube can pick up on your brand-new interest immediately after you watched... View article
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