Anaconda vs. Cloudera Data Science Workbench vs. NVIDIA RAPIDS vs. Wing Python IDE Comparison Chart

Empowering the enterprise to do real data science at speed and scale with a full-featured machine learning platform. Spend less time managing tools and infrastructure, so you can focus on building machine learning applications that move your business forward. Anaconda Enterprise takes the headache out of ML operations, puts open-source innovation at your fingertips, and provides the foundation for serious data science and machine learning production without locking you into specific models, templates, or workflows. Software developers and data scientists can work together with AE to build, test, debug, and deploy models using their preferred languages and tools. AE provides access to both notebooks and IDEs so developers and data scientists can work together more efficiently. They can also choose from example projects and preconfigured projects. AE projects are automatically containerized so they can be moved between environments with ease.

The RAPIDS suite of software libraries, built on CUDA-X AI, gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces. RAPIDS also focuses on common data preparation tasks for analytics and data science. This includes a familiar DataFrame API that integrates with a variety of machine learning algorithms for end-to-end pipeline accelerations without paying typical serialization costs. RAPIDS also includes support for multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes. Accelerate your Python data science toolchain with minimal code changes and no new tools to learn. Increase machine learning model accuracy by iterating on models faster and deploying them more frequently.