How to get started with MLOps?
How AI trailblazers implement MLOps
All about production machine learning.
Here's how the Valohai MLOps platform works.
Point to your code & data and hit run. Valohai launches workers, runs your experiments and shuts down the instances for you.
Develop through notebooks, scripts or shared git projects in any language or framework. Expand endlessly through our open API.
Automatically track each experiment and trace back from inference to the original training data. Everything fully auditable and shareable.
See how teams implement MLOps with Valohai.
Managing AI Products: Feasibility, Desirability and ViabilityHenrik Skogström / January 17, 2022
Product management is as massive a topic as machine learning so let's start with a fundamental question. When is it worthwhile to develop an AI product? A helpful tool most PMs have seen for this is the Sweet Spot for Innovation that IDEO popularized.
Running Weights & Biases Experiments on Valohai PipelinesEikku Koponen / January 14, 2022
Sometimes it is hard to combine the world of experimenting and the more dev-oriented world of data science with robust pipelines and modular work. This example combines Weights and Biases experiments with Valohai's production pipelines.
Git for Data Science: What every data scientist should know about GitJuha Kiili / January 04, 2022
Git is a tool most software developers have used daily for a decade, and with data scientists becoming an integral part of R&D teams, Git is every day for them as well. We've listed a few helpful tips on using Git for your ML work and avoiding the common pitfalls.
Data-Centric AI and How to Adopt This ApproachEikku Koponen and Jean-Emmanuel Wattier / December 20, 2021
The data you have, is, if not the most, at least close to the most valuable asset you’ve got when creating AI systems. So in practice, what can you do to embrace more data-centric AI then? We have prepared some simple steps for you to keep in mind and implement.