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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.
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.
Async & parallel job execution
Run thousands of jobs asynchronously and simultaneously to take your infrastructure to its limits
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
Boost compute efficiency 8x and retry jobs from a saved state with pre-emptible GPUs
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
Automatically wrap every job as a package with a unique UUID, containing training code, the trained model and associated artifacts