In our previous blog post about Productionizing and Deploying Data Science Projects, we discussed best practices and recommended tools that can be used in the production and deployment stages of collaborative Open Data Science workflows.
Traditional data science project deployments involve lengthy and complex processes to deliver secure and scalable applications in enterprise environments. The result is that data scientists spend a nontrivial amount of time setting up, configuring, and maintaining deployment infrastructure, which takes valuable time away from data exploration and analysis tasks in the data science workflow.
When deploying data science applications in an enterprise environment, there are a number of implementation details that must be considered, including:
- Managing runtime dependencies and project environments for each application
- Ensuring application availability, uptime, and monitoring status
- Engineering data science applications for scalability
- Sharing compute resources across an organization
- Securing data access and network communication in applications
- Managing authentication and access control of deployed applications
In this blog post, we’ll introduce the next generation of Anaconda Enterprise v5, which enables you to deploy a wide range of data science applications with a single click, including live Python and R notebooks, interactive applications and dashboards, and models with REST APIs. Anaconda Enterprise handles all of the enterprise security, scalability, and encapsulation details related to application deployments so that your data science team doesn’t have to.
Introducing the Next Generation Open Data Science Platform
Over the last few years, Anaconda and Anaconda Enterprise have been supercharging data science teams and empowering enterprise organizations with Open Data Science by enabling on-premise package management, secure enterprise notebook collaboration, data science project management, and scalable cluster workflows with Hadoop, Spark, Dask, machine learning, streaming analytics, and more.
At AnacondaCON 2017, we announced the newest capability of end-to-end data science workflows powered by Anaconda in the next generation of Anaconda Enterprise v5: secure and scalable Open Data Science deployments as part of an integrated data science experience!
Using Anaconda Enterprise, anyone on the data science team can encapsulate and deploy their data science projects as live applications with a single click, including:
- Live Python and R notebooks
- Interactive applications and dashboards using Bokeh, Datashader, Shiny and more
- Machine learning models or applications with REST APIs, including Tensorflow, scikit-learn, H2O, Theano, Keras, Caffe, and more
Using the power and flexibility of Anaconda and Open Data Science, any application, notebook, or model can be encapsulated and deployed on a server or scalable cluster, and the deployed applications can be easily and securely shared within your data science team or enterprise organization.
Data Science Deployment Functionality in Anaconda Enterprise
With a single click of the Deploy button, data scientists will be able to leverage powerful application deployment functionality in Anaconda Enterprise v5, including:
- Deploy data science projects using the same powerful 730+ libraries in Anaconda (machine learning, visualization, optimization, data analysis, and more) that your data science team already knows and loves
- Scalable on-premise or cloud-based deployment server with configurable cluster sizes
- Single-click deployment functionality for secure data science project deployments, complete with enterprise authentication/authorization and secure end-to-end encryption
- Sharing and collaboration of deployed applications that integrates with enterprise authentication and identity management protocols and services
- Data science application encapsulation, containerization, and cluster orchestration using industry-standard tooling
- Centralized administration and control of deployed applications and cluster utilization across your organization
- Connectivity to various data storage backends, databases, and formats
The new data science deployment capability in Anaconda Enterprise builds on existing features in the Anaconda platform to enable powerful end-to-end Open Data Science workflows, including on-premise package management/governance, secure enterprise notebook collaboration and project management, and scalable cluster workflows with Hadoop, Spark, Dask, machine learning, streaming analytics, and more.
The functionality in the current version of Anaconda Enterprise v4, including Anaconda Enterprise Notebooks, Anaconda Repository, and Anaconda Scale is currently being migrated to Anaconda Enterprise v5, which will be available as a GA release later this year.
Additionally, we’ll be implementing even more enterprise features to enable complete end-to-end Open Data Science workflows for your data science team, including model management, model scoring, scheduled execution of notebooks and applications, and more.
Discover Effortless Open Data Science Deployments with Anaconda Enterprise
Are you interested in using Anaconda Enterprise in your organization to deploy data science projects, including live notebooks, machine learning models, dashboards, and interactive applications?
The next generation of Anaconda Enterprise v5, which features one-click secure and scalable data science deployments, is now available as a technical preview as part of the Anaconda Enterprise Innovator Program.
Join the Anaconda Enterprise v5 Innovator Program today to discover the powerful data science deployment capabilities for yourself. Anaconda Enterprise handles your secure and scalable data science project encapsulation and deployment requirements so that your data science team can focus on data exploration and analysis workflows.
Get in touch with us if you’d like to learn more about how Anaconda Subscriptions can supercharge your data science team and empower your enterprise with Open Data Science, including data science deployments, an on-premise package repository, collaborative notebooks, scalable cluster workflows, and custom consulting/training solutions.