Saturn connects to existing storage services, real time data sources, and management tools. While Saturn is compatible with the full AWS ecosystem, you maintain full control around user access to the various data sources within your environment.
Saturn automates the DevOps and ML infrastructure engineering required to scale the full Python ecosystem. Develop Data Science and Machine Learning models in custom Jupyter environments that can leverage both CPU and GPU hardware.
With Saturn you can run models on a cluster with Dask and Kubernetes to auto-scale resources, and schedule tasks that launch asynchronously and can run in parallel with Prefect. You can deploy models as REST APIs on Saturn or as part of a separate application.
Saturn Cloud runs as an application inside Kubernetes, leveraging AWS services such as EC2, AWS Identity, Access Management (IAM), and Amazon Virtual Private Cloud (VPC) to provide secure and scalable infrastructure for running Data Science and Machine Learning workloads within your AWS environment.
– Models are deployed with Docker Containers for easy reproducibility using Amazon EKS.
– Clusters can be autoscaled with Amazon EKS and Amazon EC2.
– Jupyter Notebooks can be deployed with only one click onto your AWS environment.
Machine learning projects need to mature rapidly to keep up with business needs and most businesses are past the early stages of analytics, transitioning to more complex applications. Data science in the real world is business facing and needs to align with strategy and goals. To make that transition, models need to meet the expectations set by traditional software system.
Execution is moving deep learning into the same framework as traditional software development. Full lifecycle tools give businesses control over that execution. Saturn Cloud manages model development to deployment and maintenance. A platform that handles deep learning from start to finish is the management piece that optimizes productivity at each stage.