Advanced analytics
with saturn cloud

Advanced Analytics provide data science teams with the ability to mature machine learning projects for stable deployment, model scalability, and model maintenance as data complexity grows.    

With advanced analytics, businesses and organizations can leverage necessary development tools, rapid model iterating, training, deployment, collaboration, sharing and updating models with version control, and more.

"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 advanced analytics

Advanced Analytics

Fully Managed Infrastructure

Cloud-based Dask for scalable Python, Scikit-Learn, and NumPy workflows natively with minimal rewriting.

Advanced Analytics

Performance

Dask allows you to easily scale from single to multiple machines, optimize memory, and compute usage to maximize local resources.

Advanced Analytics

Supported Tools & Libraries

Dask for Python-native parallel processing, Jupyter, Docker and Kubernetes, Scikit-Learn, NumPy, SciPy, Bokeh, Numba, Seaborn, TensorFlow, PyTorch, Matplotlib, pandas, and more.

Advanced Analytics

Security

Deploy in your VPC with enterprise-grade security and support.

Advanced Analytics

Adaptability

Dask’s DataFrames work in the same way that Panda’s do. There’s a very low learning curve to migrate from Pandas to Dask.

Advanced Analytics

Easy to Use Interface

Quickly spin up Jupyter notebooks in the cloud and scale them according to your needs. Run your Jupyter Notebook on a VM inside AWS, Azure, or GCP without you having to know how to appropriately set up and use these services.

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.

Ready to get started?