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.

Develop
Accelerate real-world development

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.

Collaborate
Productivity and effectiveness for large teams

Build and refine models as a team in a version and source-controlled environment. AE’s collaboration features enable data scientists to focus on building models rather than spending time creating processes and defining spaces to do their work. Team members simply log onto the platform through their browsers and work in their preferred environments. With enhanced collaboration, your team can accelerate the ML lifecycle.

Govern
Thousands of open-source packages, curated by you

Data scientists download some of the most innovative open-source packages and tools for machine learning projects, but these packages are often not governed and secured by enterprise IT. AE empowers IT administrators to maintain system security and ensure MLOps are in line with enterprise policies by mirroring Anaconda’s repository and screening packages, monitoring utilization through an admin dashboard, controlling user access, and tracking package, project, and model deployment.

Deploy
Publish in one click; run anywhere

Without a platform, machine learning models can take several months and many headaches to successfully deploy. With AE, data science teams can sidestep the DevOps bottleneck and get models into REST APIs with just one click. AE accelerates time to production with deployment to pre-provisioned resources, load-balancing, DNS configuration, easy reversion back to prior models, and centralized administration of deployed apps. AE also allows for model refinement in production.

Anaconda Enterprise helps organizations harness data science, machine learning, and AI at the pace demanded by today’s digital interactions.

Implement Anywhere and Operationalize at Scale

Anaconda Enterprise can be implemented in the cloud or on premises. It is itself a cloud-native solution, supporting containerized application development and enabling organizations to do machine learning at speed and scale. All AE projects and deployments run inside of containers, automatically orchestrated and abstracted from the view of the data scientist.

AE’s cloud-native approach also makes IT’s life easy. IT admins can allow data scientists to self-serve compute resources while maintaining control. Even better, scaling AE is simple. IT admins need only run one command to add node(s) to their cluster while AE takes care of the rest.

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Use Cases

How leading organizations and industries are harnessing AI/ML

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Whitepapers

Thought leadership from the data science experts at Anaconda

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Documentation

Installation and user guide for Anaconda Enterprise 5

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The Anaconda Enterprise Support team helps maximize your investment

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