Are applications teams prepared to manage the chaos arising from an ever-growing landscape of heterogeneous deployment types? A recent survey of application and operations professionals sought to better understand how the industry is shifting and what the future of DevOps might look like. Here is what the survey uncovered ...
Anaconda 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.”
“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.”