How to get started with MLOps?
State of MLOps 2021
Where is production ML in 2021?
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.
Product Update: Human Validation and Confusion MatricesJuha Kiili / November 24, 2021
We’ve recently introduced two features that make building trusted and validated models easier: human validation steps and confusion matrices.
From Notebook to Production: How to Bridge the Gap between Data Science and Engineering?Eikku Koponen / November 08, 2021
When it comes to the production phase, actually providing the model to end-users and integrating it to the (existing) tools, Data Scientist often pass the baton to Software engineers. That handover is often quite rocky. Here are a few tips to how the bridge the gap between data science and engineering.
An End-to-End Pipeline with Hugging Face transformersEikku Koponen / November 01, 2021
This article shows an example of a pipeline that uses Hugging Face transformers (DistilBERT) to predict the shark species based on injury descriptions. With Valohai, you can easily tie together typical data science workflows into repeatable pipelines.
Machine learning lifecycle doesn’t end with the modelHenrik Skogström / October 28, 2021
Let me preface this article by saying there isn’t a single accepted definition of a machine learning lifecycle. Most articles about the machine learning lifecycle tend to focus only on a small portion of the actual lifecycle: the Experimentation loop.