There’s a new wave of automation being enabled by the combination of machine learning and smart devices. ML-enabled devices will have a profound impact on our daily lives from smart fridges to cashierless checkout and self-driving cars. …

Feasibility, Desirability and Viability — Henrik Skogström / January 17, 2022 As machine learning creeps into the mainstream of digital products, understanding the basics of machine learning is becoming more relevant to many product managers. Product people today are a rather heterogeneous bunch. …

Henrik Skogström / December 21, 2020 If you are involved with production machine learning in any way, understanding MLOps is essential. For people with software development experience, the easiest way to understand MLOps is to draw a parallel between it and DevOps. …

Not just hype. Many teams are embracing MLOps — Demographics For the State of MLOps 2021 survey, we collected 100 responses from the Valohai community. While the Valohai product offering is MLOps-related, we post content that is more broadly data science-related. Geographically, the respondents are primarily from Europe, the United States, India, and China. 27% of respondents work within the…

How Machine Learning Pipelines and Feature Stores Help Connect Data Scientists and Engineers. — Roles Are Diverging A trend we’ve been tracking for several years now is how the data science profession has steered away from being entirely independent, do-it-all unicorns into a more specialized work. It’s by no means that individuals with deep knowledge in several domains have disappeared, but rather, the need for data science…

Model deployment is simply the engineering task of exposing an ML model to real use. The term is often used quite synonymously with making a model available via real-time APIs. …

How can MLOps make consultant-client relationships more productive? There’s no doubt, AI and machine learning have come to stay. Most companies who are building software have realized this. This realization is not limited to companies developing technology-first software products, but rather it extends to the vast majority of internal applications…

Henrik Skogström / November 17, 2020 As you start incorporating machine learning models into your end-user applications, the question comes up: “When is the model good enough to deploy?” There simply is no single right answer. There is no clear-cut measure of when a machine learning model is ready to…

Henrik Skogström / October 26, 2020 What is MLOps (briefly) MLOps is a set of best practices that revolve around making machine learning in production more seamless. The purpose is to bridge the gap between experimentation and production with key principles to make machine learning reproducible, collaborative, and continuous. MLOps is not dependent on…