Dessa | Machine Learning Tools for Large Scale Experimentation

Create more with machine learning.

Build, run & monitor 1000s of ML experiments with Foundations

Foundations Suite provides tools that make every stage of the machine learning workflow faster, cheaper and better, allowing you to focus on the parts of machine learning you love.

As featured in:

Foundations Suite:

Introducing Atlas and Orbit

Automation

Run, manage & evaluate 1000s of experiments concurrently and asynchronously.

Integration

Savings

Lower your GPU compute costs by over 50% with Atlas' job scheduler.

Build and develop with Atlas

Experimentation

Run, manage & evaluate 1000s of experiments concurrently and asynchronously.

Collaboration

Collaborate across multiple teams with Atlas’s multi-tenancy features including user authentication through OpenID.

Orchestration

Lower your GPU compute costs by over 50% with Atlas' job scheduler.

More about Atlas

Deploy and monitor with Orbit

Monitoring

Track data health and consistency to spot problems before they occur.

Retraining

Use live production data and your preferred strategy to retrain and test.

Notifications

Get real-time updates through email, Slack, or PagerDuty if something goes wrong.

More about Orbit

Tried and true: What people are saying about Foundations

Advisory Systems Engineer, Dell

"Monitoring models and performing QA on production data is critical in applied AI, and the answer for that is in Foundations Orbit."

Machine Learning Engineer, Amazon

"Being in a company where we'd have to run everything locally (on prem), I really like the flexibility of Foundations Atlas."

Senior Data Scientist, Scotiabank

"Foundations Orbit’s user interface makes things so easy. This is the best integration I’ve seen yet by
a vendor."

Laura McGee,
CEO of Diversio

"Foundations Atlas allowed our team to quickly build NLP into our core product, ensuring robust insights for our customers."

Choose your own infrastructure and frameworks

Informed by real-world experience

We got our start building and deploying ML models for the world’s largest companies—and also engineering some crazy projects of our own on the side. Since day one of building our products, we’ve used our experience building real-world ML to create tools for machine learning we wanted to use but couldn’t find on the market.