The MLOps Blog

JANUARY 23, 2023

Most of our customers are doing ML/MLOps at a reasonable scale, NOT at the hyperscale of big-tech FAANG companies. Not a fork: – The MLOps team should consist of a DevOps engineer, a backend software engineer, a data scientist, + regular software folks. How about the ML engineer? Let me explain.

DevOps 59

DevOps Metadata Software Engineer Data Scientist 59

The MLOps Blog

JUNE 27, 2023

Can you see the complete model lineage with data/models/experiments used downstream? Some of its features include a data labeling workforce, annotation workflows, active learning and auto-labeling, scalability and infrastructure, and so on. MLOps workflows for computer vision and ML teams Use-case-centric annotations.

Machine Learning 134

Machine Learning Metadata Data Quality Data Scientist 134

The MLOps Blog

JULY 18, 2023

You have a bit of education in music composition, math, and science before you get more into the software engineering side of things. But you have started out in software design engineering, is that correct? But it’s absolutely critical for most people in our space that you do some type of auto-scaling.

Conversational AI 52

Conversational AI Natural Language Processing AI AI 52