Deep Learning AI

Deep Learning capabilities manage the full model development lifecycle and remove the barrier between model completion and model availability in a stable, scalable environment.    

As deep learning moves off the white board and into production, a complete platform is needed to make that jump. Saturn Cloud handles deep learning from start to finish to manage and optimize productivity at each stage. As model complexity increases, experimentation and rapid iteration will solve a range of business problems.

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.

“Dask has helped my team speed up experimentation and iteration by finishing data pre-processing tasks that used to take hours in a matter of seconds. Saturn maintains a Dask cluster so we don't have to, which frees up time for real data science. It's a huge value add.”
SENIOR Data scientist
Global eCommerce Company

Leverage Deep learning

GPU Memory

CPU/GPU & Memory

Deep learning requires Jupyter to run in more powerful environments. Saturn Cloud manages the environment resources allowing for more CPU/GPU and memory. A managed platform handling cloud resources keeps environment costs low. Data scientists can focus on building, not managing the back end.

Dask

Cloud-Based Dask

Saturn Cloud uses an open source library called Dask. Just like Saturn Cloud, Dask is completely integrated with Python. Dask DataFrames are almost identical to Pandas DataFrames. With simple function decorations, a data scientist can create custom workflows using Dask’s delayed or futures functionality.

Collaboration

DevOps & Maintenance Management

DevOps and maintenance phases are managed by the Saturn Cloud platform. Data scientists use it to access distributed resources for training, reducing iteration and final model creation time.

Local Resource

Local Resource Usage

Dask allows models to optimize local resource usage. Without additional recoding, Saturn Cloud can scale model training and inference across distributed resources. It also leverages Dask to bring the learning curve down significantly.

Deployment

Deployment

Saturn Cloud manages model deployment to AWS and removes the barrier between model completion and model availability in a stable, scalable environment. Saturn can be deployed in phases. Individual team members can start using features when they make sense to adopt. Code migration can happen over time, project by project or as part of normal model maintenance.

Lifecycle Tools

Full Lifecycle Tools

The initial phases of the lifecycle are a heavy time sink if they’re done in silos. A model that lives on a data scientist’s laptop isn’t trackable or reviewable, therefore making it difficult for data science team leadership and project leadership to gauge progress. Saturn Cloud manages those phases to create more transparency.

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