The first learning from big tech is: Most large companies — wanting to adopt AI — hire teams to build internal platforms for ML practitioners. But these data scientists or ML engineers are often not familiar with enterprise software engineering. Expecting them to learn is feasible but inefficient, which is where MLOps platforms come in ...
MLOps
June 29, 2022
Machine learning operations (MLOps), combining ML and software engineering, are becoming more mainstream and intend to improve the quality and speed of delivering ML models to production. Business leaders are missing out on many opportunities without proper MLOps platforms in place. So, how can companies get started? ...
June 27, 2022
ML engineering can be defined as the technical systems and processes associated with the stages of the ML lifecycle (also referred to as MLOps cycle) from data preparation, modeling building, and production deployment and management. While ML engineering entails the provisioning, deployment, and management of infrastructure that enables model building, data labeling, and model inference, it can go much deeper than that ...
December 07, 2021
The Holiday Season means it is time for DEVOPSdigest's annual list of DevOps predictions. Industry experts — from analysts and consultants to users and the top vendors — offer thoughtful, insightful, and often controversial predictions on how DevOps and related technologies will evolve and impact business in 2022 ...
October 28, 2021
DEVOPSdigest asked the top minds in the industry what they think AIOps can do for DevOps and developers. Part 4 covers cloud and containers ...
