Models are temporary, pipelines are forever.

Train, Evaluate, Deploy, Repeat. Valohai is the only MLOps platform that automates everything from data extraction to model deployment.

ML Pipeline

End-to-end ML pipelines

Automate everything from data extraction to model deployment.

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Model Library

Model library

Store every single model, experiment and artifact automatically.

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Model Deployment

Model deployment

Deploy and monitor models in a managed Kubernetes cluster.

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MLOps eBook

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Practical MLOps

How to get started with MLOps?

State of MLOps report


State of MLOps 2021

Where is production ML in 2021?



All about production machine learning.

Here's how the Valohai MLOps platform works.

Managed MLOps

Managed MLOps

Point to your code & data and hit run. Valohai launches workers, runs your experiments and shuts down the instances for you.

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Integrate anywhere

Integrate everywhere

Develop through notebooks, scripts or shared git projects in any language or framework. Expand endlessly through our open API.

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Teamwork boosters

Full reproducibility

Automatically track each experiment and trace back from inference to the original training data. Everything fully auditable and shareable.

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Join the companies taking their ML to the next level.

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Latest blog posts

Observability in Production: Monitoring Data Drift with WhyLabs and Valohai

Eikku Koponen / December 02, 2021

Observability is the collection of statistics, performance data, and metrics from every part of your ML system. Metadata, if you will. We will dig into how we can easily get started with observability and detect data drift using whylogs while executing your pipeline on Valohai.

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Product Update: Human Validation and Confusion Matrices

Juha Kiili / November 24, 2021

We’ve recently introduced two features that make building trusted and validated models easier: human validation steps and confusion matrices.

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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.

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An End-to-End Pipeline with Hugging Face transformers

Eikku 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.

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