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Anaconda Enterprise

Name: Anaconda Enterprise
Rating: 4.5 (2 reviews)

By Anaconda

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Highlights : About Anaconda Enterprise

Manage and Share Data Science Projects and Dependencies

Self-Service Deployment of Models, Notebooks, and Dashboards

Centralized User, Role, or Group-Based Access Control Management and Confuguration

Distribute Anaconda Libraries across Hadoop and Spark Clusters

On-Premise Data Science Package Repository

Token-Based Access to Data Science Models and Applications

About Anaconda Enterprise

Anaconda Enterprise is a modern and dynamic data science software platform that allows teams of data scientists to create, supervise, and automate AI-powered data science models and pipelines across production environments and server clusters. Anaconda Enterprise is equipped with collaboration features which enable team members to share data science projects with each other and edit them in real time. They can also make their resources readily accessible to everyone in their team such as machine-learning models, online notebooks, dependencies, and dashboards. Anaconda Enterprise is a scalable data science solution which means users will be able to quickly provision their data science projects and applications with all the necessary computation resources they need. The platform permits them to deploy their data science models and resources to either on-premises or cloud-based environments. With Anaconda Enterprise, the tools, data science model versions, and packages being utilized by teams can be easily managed, governed, and controlled. The platform can generate logs that record the activities being performed by each team member. For a more secure collaboration, Anaconda Enterprise implements TTL or SSL encryption so that team members can communicate and collaborate over their network with confidence, without being bothered by security breaches and issues like data leakage, data theft, and more.

Specifications

24/7 Support

Yes

Business Size

Mid-Market

Deployments

Cloud

Language Support

English

Platforms

Product Details

Features

Collaboration

Browser-Based Notebook Collaboration

Manage and Share Data Science Projects and Dependencies

Scalable Distribution of Computational Resources

Self-Service Deployment of Models, Notebooks, and Dashboards

Remote Deployment to Hadoop or Spark Clusters

Governance

Control Packages, Versions, and Tools

Event Logging and Tracking

Integration with Identity Providers

Centralized User, Role, or Group-Based Access Control Management and Confuguration

Integrated Jupyter Data Science Environements

Versioning and Access Control

Scalable

Distribute Anaconda Libraries across Hadoop and Spark Clusters

One-Click Deployment

On-Premise And Cloud Deployments

On-Premise Data Science Package Repository

License Filtering and Auditing

Security

TLS/SSL Encryption

Token-Based Access to Data Science Models and Applications

Benefits

Enterprise-Ready Data Science Platform

Anaconda Enterprise is an enterprise-ready data science platform. But what is data science really all about? Data science is a field that applies various theories, methods, systems, and processes from different fields and disciplines in order to gain insights and knowledge from data. It combines scientific methods, mathematics, statistics, information science, and computer science; and can be applied to activities related to business intelligence, business analytics, and predictive modeling. Anaconda Enterprise automates how organizations and businesses implement data science projects, models, and processes; helping them perform data analytics much better.

Real-Time Collaboration

Collaboration is one of the key features offered by Anaconda Enterprise. It unifies data science projects and the corresponding resources in one central location and permits team members to collaborate in real time. Team members can share data science projects and dependencies with their colleagues and the latter can access all of them from a convenient place.

Access Convenient Data Science Environments Through Jupyter Integration

As part of its real-time collaboration capabilities, Anaconda Enterprise integrates with Jupyter-based software solutions which include Jupyter Notebooks and JupyterLab. Jupyter Notebooks is an open-source and open-standards web-based tool which allows users to build and share documents for data visualization, data cleaning, numerical simulation, machine learning, or statistical modeling. These documents are called notebooks or web-based documents that contain live codes, equations, data visualizations, and narrative texts. Meanwhile, JupyterLab is a computational environment that provides an intuitive user interface for managing Jupyter projects. Because Anaconda Enterprise supports integration with these solutions, users will be able to collaborate directly on the Jupyter web-based documents they are sharing with their team members. In addition, the platform lets them handle different versions of their browser-based notebooks as well as control how those notebooks are being accessed.

Scalable Architecture That Can Automatically Adjust

The enterprise data science platform has a scalable architecture, and that can be observed in its ability to distribute Anaconda Enterprise libraries and resources across the clusters of servers handled by Hadoop and Apache Spark. Hadoop is a big-data framework which distributes large collections of data across multiple nodes within a cluster of servers. When it comes to Apache Spark, this is also a big-data framework which is used for processing those data collections. As a scalable data science solution, Anaconda Enterprise can automatically scale up or down depending on the number of cluster nodes that users need to distribute data collections to. For example, users will be able to instantly add new cluster nodes or delete existing ones.

Self-Service Deployment Features

Anaconda Enterprise is built with amazing deployment features. For example, it has a self-service deployment capability which enables users to gain full flexibility and control over the deployment of AI-powered data science models, dashboards, and browser-based notebooks. With just a single click, users can deploy any of these. Another deployment feature available in Anaconda Enterprise is its remote deployment capability. Here, users will be able to remotely deploy their data science projects and models, and computational resources to Hadoop or Apache Spark server clusters. Deployments can also be easily managed for on-premise environments and cloud-based services such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud.

Complete Governance Of Data Science Packages, Versions, And Tools

Governing data science projects, models, and resources is made easy using Anaconda Enterprise. It has an online repository where on-premises data science packages and stacks can be stored. Furthermore, whatever tools, packages, and versions data analysts and scientists are utilizing; the platform makes sure that they are able to control them.

License Management, Auditing, And Reporting

The data science platform also has license management feature. It lets users filter licenses associated with their data science projects and applications. In addition, the platform has the ability to generate reports as they audit their licenses.

Event Logging And Tracking

Another governance feature of Anaconda Enterprise is that it enables users to record and track all activities and events related to their data science projects, packages, and deployments. Thus, these events and activities can be logged and audited effortlessly.

Token-Based Acess

Anaconda Enterprise provides security features which help teams guarantee that their projects and resources are accessed only by authorized individuals. The platform uses a token-based system of accessing the deployed data science models and applications. In other words, users will be able to utilize their models and applications without exposing any sensitive data or information. In a token-based system, data or information are represented by tokens. Then, these tokens are the ones being stored and processed instead of the original data or information.

Centralized Management Of Access Controls

Furthermore, Anaconda Enterprise centralizes the management of access controls and configurations. Whether users are handling access on a per user, role, or group basis; they can do this in a central location. Additionally, the enterprise data science solution supports integration with identity providers like LDAP, Active Directory, SAML, and Kerberos. Thus, users can access their data science projects, models, and resources using their credentials and profiles from their existing identify provider; streamlining user authentication and access.

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