New Release of Anaconda Enterprise features Expanded GPU and Container Usage
Anaconda, Inc. is thrilled to announce the latest release of Anaconda Enterprise, our popular AI/ML enablement platform for teams at scale. The release of Anaconda Enterprise 5.2 adds capabilities for GPU-accelerated, scalable machine learning and cloud-native model management, giving enterprises the power to respond at the speed required by today’s digital interactions.
Anaconda Enterprise—An AI/ML 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 Enterprise is the only product on the market that allows data scientists to go from laptop for model development to a 1000-node GPU cluster for training to production deployment—all with full reproducibility and governance.
Embracing the Shift from Traditional Big Data Services to Cloud-Native Architectures
As enterprises continue to shift from traditional big data services like Hadoop and Spark to containers and cloud-native style architectures, data science teams are pivoting as well, looking for solutions that provide actionable insights to drive their businesses forward.
Anaconda Enterprise employs 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 cannot scale. Anaconda Enterprise integrates directly with an organization’s authentication, source code control, and data lakes and ensures end-to-end governance and control.
Accelerating Scalable, Deployable Machine Learning with GPUs
World-class machine learning requires petaflop-scale model training, made economically viable by GPUs, and automated deployment into production IT environments. Anaconda Enterprise enables data scientists 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 the single click of a button.
Anaconda Enterprise is the AI enablement platform that provides the foundation for AI/ML libraries and toolkits (including TensorFlow, Scikit-Learn, MXNet, and H2O.ai), empowering organizations to deploy and manage them quickly and easily. New features in version 5.2 include:
- Cloud-native scale/GPU accelerated capabilities—Securely and efficiently share a central cluster of GPUs, providing a faster and more cost-effective way to train complex models
- Job scheduling—Automate one-off or regularly recurring deployments
- External Git integration—Use existing source code control systems and Continuous Integration (CI) tools, such as Bitbucket or Git
Taking Organizations to the Next Level
As the industry standard for data science, we make it our mission to build the best end-to-end platform for data scientists and the organizations they serve. Anaconda Enterprise is the only platform to combine core AI technologies, automated governance and reproducibility, and cloud-native approaches to make data science teams as productive as possible. Anaconda Enterprise empowers organizations to develop, govern, and automate ML/AI pipelines from laptop to production, quickly delivering insights into the hands of business leaders and decision-makers.