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Anaconda Enterprise Unveils New Release Focusing on Supporting Expanded GPU and Container Usage

Anaconda, Inc., the most popular Python data science platform provider with 2.5 million downloads per month, today announced the availability of Anaconda Enterprise 5.2. This latest release adds capabilities for NVIDIA GPU-accelerated, scalable machine learning and cloud-native model management to Anaconda's popular AI enablement platform for teams at scale.

"As enterprises transition to new technologies like containers and orchestration frameworks, organizations are pivoting to take advantage in areas such as data science and machine learning," said John L Myers, Managing Research Director Business Intelligence at Enterprise Management Associates (EMA). "Encapsulating the complexity of data management and model deployment from data scientists with platforms such as Kubernetes and Docker allows data science teams to scale to meet the ML model goals of business stakeholders. An AI/ML enablement platform, such as Anaconda Enterprise, will enable organizations to make this streamlined process a reality."

AI Enablement Platform For Teams At Scale

Anaconda Enterprise is a software platform for developing, governing, and automating data science and AI pipelines from laptop to production. It is the de-facto standard for data science and machine learning, with over 6 million data scientists using its open source solution locally to develop and score models on a subset of data. Anaconda is the only product on the market that allows data scientists to go from laptop for model development to a 1,000-node GPU cluster for training, to production deployment with full reproducibility and governance.

"Data scientists require their AI models to be deployed into production to propel their organizations forward. However, world-class machine learning requires petaflop-scale model training, made economically viable by GPUs, and automated deployment into production IT environments," said Mathew Lodge, SVP of products and marketing, Anaconda Inc. "With Anaconda Enterprise 5.2, we're enabling those within the enterprise to train models on the full data set at scale, including scheduling to make effective use of GPUs, and then deploy to production with one click. All without having to become an expert in containers, DevOps and Kubernetes."

Anaconda Enterprise uses cloud native approaches, including Docker and Kubernetes, to scale data science and machine learning across teams and clusters while simplifying and automating AI/ML governance and reproducibility. For IT leaders, Anaconda Enterprise ensures the highest productivity environment for data scientists without forcing them into "walled garden" approaches that don't scale. Anaconda Enterprise integrates directly with the organization's authentication, source code control, and data lakes and ensures end-to-end governance and control.

Anaconda Enterprise is the AI enablement platform that provides the foundation for AI/ML libraries and toolkits (e.g., TensorFlow, Scikit-Learn, MXNet, PyTorch and XGBoost), empowering organizations to deploy and manage them quickly and easily.

"Cloud native technologies deliver dramatic improvements to software velocity, quality and scale for organizations of any size. Fortunately, these benefits also applied to the data science space," said Dan Kohn, executive director of the Cloud Native Computing Foundation. "Platforms like Anaconda Enterprise, built on Kubernetes, make it possible for data scientists and IT teams to modernize their operations and support agile, cloud native infrastructures."

"NVIDIA GPU acceleration has become critical for deep learning as models have become more complex and datasets have grown in size. Anaconda Enterprise enables enterprises to more effectively use NVIDIA GPUs to boost the productivity of data scientists," said Joshua Patterson, Director of AI Infrastructure at NVIDIA.

For additional information on Anaconda Enterprise, please visit:

Published Tuesday, July 17, 2018 9:51 AM by David Marshall
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