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.
Cloud-based Dask for scalable Python, Scikit-Learn, and NumPy workflows natively with minimal rewriting.
Dask allows you to easily scale from single to multiple machines, optimize memory, and compute usage to maximize local resources.
Dask for Python-native parallel processing, Jupyter, Docker and Kubernetes, Scikit-Learn, NumPy, SciPy, Bokeh, Numba, Seaborn, TensorFlow, PyTorch, Matplotlib, pandas, and more.
Deploy in your VPC with enterprise-grade security and support.
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.
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.