From its inception, Anaconda has had the mission to build better products for data scientists and the organizations they serve. We were the first company to launch a collaborative notebook product back in 2012. In this 5-year journey, we’ve learned a lot from our customers and we’ve worked to empower their organizations to effectively leverage open source data science in their enterprise environments. We have been privileged to work closely with our customers as they were building their new data science teams, defining best practices and searching for products to solve new challenges they faced.

Today, we are proudly announcing the fruits of that work, the next generation of our data science platform: Anaconda Enterprise 5.

Anaconda Enterprise 5 new capabilities include:

  • Integrated data science experience for the entire organization
  • Collaboration and reproducibility with JupyterLab and Anaconda Project
  • One-click data science deployment
  • Scalable architecture for on-premises and cloud deployments

The first data science platform built with the entire organization and development cycle in mind

Anaconda Enterprise 5 is built for your entire organization and serves the needs of Data Scientists, IT Administrators, Business Analysts & Managers, Developers and Machine Learning Engineers.

With Anaconda Enterprise 5, organizations can control the full data science development cycle: from building proprietary data science software to empowering managers and analysts to consume the outcomes of data science analysis.

Data Scientists can create and upload new projects, collaborate with their peers, reproduce their analysis locally for any operating system and deploy with a single click of a button.

IT administrators get a governed, secure and scalable system that ties to their enterprise identity providers (LDAP, AD, SAML, Kerberos) and a built-in Operations Center to manage usage and access to the platform

Business analysts and managers can self-serve, run interactive reports and dashboards, and understand and use data to make better business decisions.

Developers & Machine Learning Engineers can build and publish their own software as conda packages and make them easily consumable by data scientists in their projects. Application developers are able to consume ML REST API in their web applications to embed intelligent capabilities.

Data science collaboration and reproducibility: JupyterLab and Anaconda Project

Anaconda Enterprise 5 includes JupyterLab, the next generation data science IDE. With JupyterLab, data scientists have an extensible, one-stop solution for all their data science development needs: notebooks, terminal, interactive Python, markdown editor and viewer, extension for file readers like GeoJSON or CSV. Check out our recent webinar to learn more about JupyterLab.

For data science reproducibility, Anaconda Enterprise leverages Anaconda Project, an open source project that extends package requirements beyond package dependencies, including data, deployment commands, environment variables and credentials. Learn more about Anaconda Project in our previous blog post and documentation.

One-click data science deployment

One of the hardest tasks for data scientists is moving their data science to production so that other members of their organization can leverage their work, make better decisions or build smarter applications. With Anaconda Enterprise, a wide variety of data science outputs can be deployed with a single click of a button: notebooks, dashboards, ML REST APIs and web applications.

Anaconda Enterprise takes care of provisioning servers, installing the right project dependencies, access control and permissioning, and the scalability and reliability of the deployed project. We are making data scientists at organizations more productive by covering these tasks and allowing them to focus their attention to what they are best at: getting insights from data, predicting and modeling.

Learn more about data science deployment in Anaconda Enterprise 5 in our previous blog post “Secure and Scalable Data Science Deployment” and webinar.

Leveraging Docker and Kubernetes for on-premises and cloud deployments

We have re-architected our platform and built Anaconda Enterprise 5 on Docker and Kubernetes to meet the scalability and reliability requirements of our customers. Anaconda Enterprise can be deployed on-premises (incl. airgapped environments) or Cloud environment. With Kubernetes becoming the standard for multi-cloud and on-premises deployment of applications, Anaconda Enterprise allows our customers to scale across a distributed infrastructure, and meet their scalability needs today and in the future.

Summary

With Anaconda Enterprise 5, organizations get:

  • The most comprehensive data science development cycle: from algorithm development, building and packaging for engineers to self-service dashboards and reports for managers.
  • The widest range of deployment options, including notebooks (Python, R, Spark), Machine Learning REST APIs, Interactive Applications (BokehApps and ShinyApps), general web applications (Django and Flask) and the ability to combine any of those in composable applications.
  • A unique continuity of experience and reproducibility from their local desktop development environments with their favorite data science editors, JupyterLab and Jupyter Notebooks, and the platform of their choice: Linux, Windows, macOS. There’s no vendor lock-in, and all data science projects use the open source standard: Anaconda Project, compatible with the free Anaconda Distribution.
  • The only vendor to fully support Anaconda Distribution for your enterprise needs. With Anaconda Support, enterprises reduce risk of intrusions, vulnerabilities and infringement, get questions answered fast from our Anaconda Experts and help with resolving any issues quickly to minimize downtime.

Sign up for a free 30-day test drive to experience Anaconda Enterprise 5. Read our online documentation, and contact [email protected] for more information.


About the Author

Christine Doig

Sr. Data Scientist, Product Manager

Christine is a Senior Data Scientist at Anaconda. She has over five years’ experience in analytics, operations research and machine learning in a variety of industries, including energy, manufacturing and banking. At Anaconda, she worked …

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