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Atlas: Development tools for high-performance data scientists.

You've put ML on the map for your organization.

Now it's time to scale it.
With Atlas, create business value faster than ever before.



What can Atlas do for you
and your team?

Atlas enables machine learning teams to manage thousands of
experiments efficiently, speed up the development lifecycle, automate complex
infrastructure challenges, save on compute costs and free DevOps teams from
model deployment hurdles.

Atlas enables machine learning teams to manage thousands of experiments efficiently, speed up the development lifecycle, automate complex infrastructure challenges, save on compute costs and free DevOps teams from model deployment hurdles.

Built to save costs

Atlas makes it possible to cache, use preemptible GPUs and get access to compute on-demand, ensuring you get your money’s worth with your infrastructure.

Built for efficiency & reproducibility

Run 1000’s of experiments concurrently to 10x your productivity. Use Atlas’ model packaging feature to share everything required to run your model. Never worry again about reproducibility.

Built for collaboration

Get a holistic view of all model development efforts in your organization with Atlas’ built-in multitenancy. Remove development silos and increase transparency in your organization.

Built for flexibility

Atlas was built to be lightweight, highly decoupled, platform and framework-agnostic – allowing you to maintain your current workflow without sacrificing on any features.

Built with the best in the business

Atlas Features

Async & parallel job execution

Run thousands of jobs asynchronously and simultaneously to take your infrastructure to its limits

Job dashboard

Access-controlled dashboards that allow you to view all on-going ML projects and manage their associated jobs

Built-in optimization paradigms

Run hundreds of architecture and parameter search jobs effortlessly

Compute orchestration

Boost compute efficiency 8x and retry jobs from a saved state with pre-emptible GPUs

Method-level caching

Save even more on compute costs with method-level caching

Resource usage reporting

An easy way to find out the cost of each job

Instant resource access

On-demand GPU and compute resource management for single-node job deployments

Slack and email integration

Get real-time notifications about your jobs’ status in a channel where you know you’ll see them

Model packaging

Automatically wrap every job as a package with a unique UUID, containing training code, the trained model and associated artifacts

See what Atlas can do.
Get in touch or sign up for a trial to
discover all of Atlas’ features.