Anaconda Enterprise - Detailed Review
Data Tools
Anaconda Enterprise - Product Overview
Introduction to Anaconda Enterprise
Anaconda Enterprise is a comprehensive data science platform developed by Anaconda, Inc., a leading company in the data science and AI sector. Here’s a brief overview of its primary function, target audience, and key features:Primary Function
Anaconda Enterprise is built to support the entire data science development cycle within an organization. It enables data scientists, IT administrators, business analysts, and other stakeholders to develop, deploy, and manage data science projects efficiently. The platform focuses on integrating data science workflows into the existing infrastructure of enterprises, facilitating better decision-making and smarter applications.Target Audience
The primary target audience for Anaconda Enterprise includes:- Data Scientists
- Machine Learning Engineers
- Researchers
- Enterprise IT Departments
- Data Analysts
- Statisticians
- Business Analysts and Managers
- Various roles within academic, educational, and government institutions, as well as consulting firms and healthcare organizations.
Key Features
Anaconda Enterprise offers several key features that make it a valuable tool for enterprises:Comprehensive Data Science Development Cycle
The platform supports the entire development cycle, from algorithm development and building to deployment and scaling. It allows data scientists to create and upload projects, collaborate with peers, and reproduce analysis locally on any operating system. Projects can be deployed with a single click, including notebooks, dashboards, ML REST APIs, and web applications.Security and Governance
Anaconda Enterprise provides a governed, secure, and scalable system that integrates with enterprise identity providers (LDAP, AD, SAML, Kerberos). It includes a built-in Operations Center for managing usage and access to the platform, ensuring compliance with regulatory standards.Collaboration and Deployment
The platform facilitates collaboration among data scientists, IT administrators, and business analysts. It offers one-click deployment options for various data science outputs, including notebooks, dashboards, and machine learning models. This simplifies the process of moving data science projects to production, allowing other members of the organization to leverage the insights and build smarter applications.Integration and Compatibility
Anaconda Enterprise ensures continuity of experience and reproducibility from local desktop development environments to the platform. It supports popular data science editors like JupyterLab and Jupyter Notebooks and is compatible with various operating systems (Linux, Windows, macOS). The platform also avoids vendor lock-in by using open-source standards like Anaconda Project.Advanced Analytics and AI Tools
The platform provides access to a rich ecosystem of AI tools, including advanced analytics capabilities, scalable machine learning models, and robust data management tools. This empowers enterprises to drive innovation and gain a competitive advantage by leveraging the full potential of AI. By offering these features, Anaconda Enterprise helps organizations streamline their data science initiatives, enhance collaboration, and accelerate the deployment of data-driven solutions. Anaconda Enterprise - User Interface and Experience
User Interface
Anaconda Enterprise features a well-organized and accessible interface that simplifies the workflow for its users. Here are some key aspects of its user interface:
Centralized Management
The platform provides a central location where users can manage their data science projects, resources, and dependencies. This includes easy access to tools like Jupyter Notebooks and JupyterLab, which are integrated into the platform for interactive coding and collaboration.
Package Management
The interface includes a straightforward package management system that allows users to browse, install, and update various libraries and dependencies with ease. This feature is particularly useful for installing essential packages like TensorFlow, PyTorch, or Scikit-learn.
Environment Management
Users can create and manage different environments for their projects, ensuring that dependencies do not clash. This feature is crucial for maintaining consistency and facilitating collaboration among team members.
Real-Time Collaboration
The integration with Jupyter-based solutions enables real-time collaboration on web-based documents. Users can share and work on notebooks, handle different versions, and control access to these documents seamlessly.
Ease of Use
Anaconda Enterprise is designed to be highly user-friendly, making it accessible to both novice and seasoned professionals:
Intuitive Navigation
The platform offers an intuitive user interface that reduces the learning curve. Users can easily find and use the tools they need without extensive training.
Streamlined Workflow
Features like streamlined version control and easy deployment of data science models and resources help maintain a consistent workflow. This reduces conflicts and promotes a more orderly development process.
Data Visualization
The interface includes tools for data visualization, such as Matplotlib and Seaborn, which allow users to quickly generate insightful graphs and charts. This is essential for communicating complex data patterns effectively.
Overall User Experience
The overall user experience of Anaconda Enterprise is positive, with several key benefits:
Collaboration
The platform unifies data science projects and resources in one central location, facilitating real-time collaboration among team members. This enhances productivity and ensures that all team members are on the same page.
Scalability
Anaconda Enterprise has a scalable architecture that allows users to distribute resources across server clusters using frameworks like Hadoop and Apache Spark. This scalability ensures that the platform can adjust to the needs of growing development and data science teams.
Security and Compliance
The platform implements security measures such as TTL or SSL encryption, ensuring secure collaboration and protecting against data breaches and other security issues.
In summary, Anaconda Enterprise offers a user-friendly interface that simplifies the management of data science projects, enhances collaboration, and provides a seamless user experience. Its intuitive design and powerful tools make it an invaluable resource for AI developers and data scientists.
Anaconda Enterprise - Key Features and Functionality
Anaconda Enterprise is a comprehensive data science platform that integrates AI and machine learning to facilitate various aspects of data science projects. Here are the main features and their functionalities:
Collaboration and Real-Time Sharing
Anaconda Enterprise enables real-time collaboration among team members. It allows users to share data science projects, dependencies, machine-learning models, online notebooks, and dashboards in a central location. This feature facilitates teamwork and ensures that all team members have access to the necessary resources from a convenient place.
Integration with Jupyter
The platform integrates with Jupyter-based solutions, including Jupyter Notebooks and JupyterLab. This integration allows users to build, share, and collaborate on web-based documents that contain live code, equations, data visualizations, and narrative texts. Users can manage different versions of these notebooks and control access to them.
Scalable Architecture
Anaconda Enterprise has a scalable architecture that can automatically adjust to the needs of the users. It can distribute resources across clusters of servers handled by Hadoop and Apache Spark, allowing users to add or remove cluster nodes as needed. This scalability ensures that data science projects and applications can be quickly provisioned with the necessary computational resources.
Self-Service Deployment
The platform offers self-service deployment capabilities, allowing users to deploy AI-powered data science models, dashboards, and browser-based notebooks with a single click. Users can also remotely deploy their projects and models to Hadoop or Apache Spark server clusters, as well as to on-premises and cloud-based environments like AWS, Microsoft Azure, and Google Cloud.
Governance and Management
Anaconda Enterprise provides comprehensive governance features for managing data science projects, models, and resources. It includes an online repository for storing on-premises data science packages and stacks. The platform also offers license management, auditing, and reporting capabilities, allowing users to filter licenses, generate reports, and log all activities related to their projects.
Security Features
The platform implements several security features to ensure secure collaboration. These include TTL or SSL encryption to protect against security breaches and data leakage. Additionally, Anaconda Enterprise uses a token-based access system, where data or information is represented by tokens to prevent the exposure of sensitive data.
AI Integration
Anaconda Enterprise is deeply integrated with AI technologies, enabling the automation of AI-driven data science models and pipelines. It supports advanced analytics, scalable machine learning models, and robust data management tools. This integration helps organizations automate AI at scale, driving innovation and competitive advantage.
Event Logging and Tracking
The platform allows users to record and track all activities and events related to their data science projects, packages, and deployments. This feature ensures that all events can be logged and audited effortlessly, enhancing transparency and accountability.
Cloud and On-Premises Deployment
Anaconda Enterprise supports deployment to both on-premises and cloud-based environments. This flexibility allows organizations to choose the deployment method that best fits their infrastructure and security requirements.
By combining these features, Anaconda Enterprise provides a comprehensive and secure environment for data scientists, developers, and business analysts to collaborate, develop, and deploy AI-driven data science projects efficiently.
Anaconda Enterprise - Performance and Accuracy
When Evaluating Anaconda Enterprise
When evaluating the performance and accuracy of Anaconda Enterprise in the AI-driven data tools category, several key points stand out:
Performance
Anaconda Enterprise is built to provide high-performance capabilities, particularly in areas such as data processing, analytics, and machine learning. Here are some highlights:
- The platform benefits from performance improvements due to the incorporation of advancements from Intel, such as enhanced random number generation and mathematical functions. This has resulted in significant performance gains, with Anaconda’s Python builds showing a 10-20% improvement over Ubuntu’s Python and 30-40% over earlier builds.
- Anaconda’s use of advanced compilers and optimization techniques, like link-time optimization and profile-guided optimization, further enhances performance.
- The platform is optimized for multithreading, utilizing libraries like Intel Threading Building Blocks, which can leverage multiple cores for greater speed-up.
Accuracy
Accuracy is a critical aspect of Anaconda Enterprise, especially in AI and machine learning applications:
- Anaconda’s tools are capable of assimilating diverse data sources to uncover hidden patterns, predict trends, and provide actionable insights with high accuracy. This is achieved through advanced analytics and machine learning models.
- The Anaconda Assistant, a key component of the platform, has undergone significant improvements through Evaluations Driven Development (EDD). This process involves rigorous testing, prompt engineering, and Agentic Feedback Iteration to refine the Assistant’s performance in explaining and fixing errors. This has led to a substantial increase in the accuracy of generated responses and error explanations.
Limitations and Areas for Improvement
While Anaconda Enterprise offers strong performance and accuracy, there are some limitations and areas that require attention:
- Disk Latency: Anaconda Enterprise is sensitive to disk latency, which can impact performance. It is recommended to use better disks for certain directories to mitigate this issue.
- Initial Setup: There are specific workarounds needed during the initial setup, such as creating a project before attempting to create an environment. This ensures that the environment creation process completes successfully.
- Continuous Evaluation: The EDD process, while effective, is ongoing and requires continuous evaluation and refinement to maintain and improve the accuracy of AI-powered tools like the Anaconda Assistant.
Conclusion
In summary, Anaconda Enterprise demonstrates strong performance and accuracy, driven by its advanced tools and methodologies. However, users should be aware of potential limitations related to hardware requirements and initial setup procedures. Continuous evaluation and refinement are key to maintaining and enhancing the platform’s capabilities.
Anaconda Enterprise - Pricing and Plans
Free Tier
Anaconda does offer a free tier, known as the Anaconda Distribution (previously Individual Edition). This tier is free and includes:- Access to an expertly-curated and privately hosted repository
- Utilities to easily search, install, and manage thousands of open-source packages
- Forum/community support, FAQ/knowledgebase, and social media support.
Starter Tier (Pro)
The Pro tier, or Anaconda Professional, is priced at $9 per month. It includes:- Professional-grade repository
- Tokenized user authentication
- Package usage reporting
- Commercial compliance
- Phone and live chat support, which are not available in the free tier.
Business Tier
The Business tier, or Anaconda Business, is priced at $50 per month per user. This tier includes:- Professional-grade distribution with features enabling security and IT teams to meet security and compliance requirements
- Additional features to support development and data science teams without compromising on security.
Enterprise Tier
The Enterprise tier, or Anaconda Server (previously Team Edition), is priced at $60 per month per user. It offers:- All the features of the Business tier
- The ability to leverage these features on private infrastructure
- A centralized location to access, manage, and create custom repositories
- Additional tier-based add-ons such as site-wide licensing, custom private mirroring, custom installers, technical support services, and professional services.
Additional Services
Anaconda also provides premium support options, including personalized consulting and training sessions, to help users maximize the benefits of their tools and integrate them into their workflows more effectively.No Free Trial for Enterprise
It’s important to note that Anaconda Enterprise does not offer a free trial for its premium plans. This tiered approach allows users to select the plan that best fits their specific needs, whether they are individuals, educational institutions, or large enterprises. Anaconda Enterprise - Integration and Compatibility
Integration with Other Tools
Anaconda Enterprise is built to work harmoniously with a wide range of tools and technologies. For instance, it integrates well with Panel, an open-source Python tool, to enable one-click deployment and easy sharing of applications and dashboards.
It also supports the deployment of machine learning models and inference APIs, allowing these models to integrate with software applications, web services, and IT infrastructure. This facilitates a streamlined approach to deploying complex data science models.
Collaboration and Team Workflows
The platform is designed to foster collaboration among data scientists, developers, and business analysts. It provides tools for team collaboration, ensuring that efforts are not duplicated and priorities are aligned. This includes features like version controls, error tracking, and logging to maintain organized data science workflows.
Compatibility Across Platforms
Anaconda Enterprise can be installed on various Linux distributions, including RHEL/CentOS 7.x, 8.x, Ubuntu 16.04, and SUSE 12 SP2, SP3, SP5. It is crucial that all cluster nodes operate the same version of the operating system to ensure smooth installation and operation.
Cloud and On-Premises Deployment
The platform supports deployment on-premises, on hosted VSphere, or on cloud servers. The installation requirements are consistent across these environments, although cloud-specific performance requirements need to be met. This flexibility allows organizations to choose the deployment method that best suits their needs.
GPU Support
For GPU-enabled deployments, Anaconda Enterprise supports specific versions of the NVIDIA CUDA driver (CUDA 10.2, 11.2, 11.4, 11.6) and recommends using Tesla V100 or Tesla P100 models. This ensures that GPU worker nodes can be effectively utilized for compute-intensive tasks.
Security and Governance
The platform emphasizes security and governance, allowing for the configuration of access control and ensuring complete reproducibility from laptop to cluster. It also supports air-gapped environments, isolating the computing infrastructure from external networks for enhanced security.
Client Environment Compatibility
Users can interact with Anaconda Enterprise from various client environments, including Windows, macOS, or Linux. This ensures that the platform is accessible and usable across different user setups.
Conclusion
In summary, Anaconda Enterprise is highly integrative and compatible with a variety of tools, platforms, and devices, making it a versatile and secure choice for data science and AI projects in enterprise environments.
Anaconda Enterprise - Customer Support and Resources
Customer Support Options
Anaconda Enterprise offers a comprehensive suite of customer support options and additional resources to ensure users can effectively utilize their data science and machine learning tools.Support Packages
Anaconda Enterprise provides two main support packages: Standard Support and Premium Support.Standard Support
This package is included with all valid subscriptions. It offers support via the Support Portal, with standard hours of operation being 12×5 (Monday to Friday, 6am to 6pm U.S. Central time). Users can designate 1 to 2 contacts to manage support tickets. The response time for urgent issues (Sev 1) is less than 1 business hour, while other severities have varying response times.Premium Support
This package includes all the features of Standard Support plus additional benefits. It offers 24×7 support for urgent (Sev 1) issues, unlimited support requests per month, and up to 5 designated support contacts. Premium Support also includes advanced professional services support (up to 12 hours annually), live training sessions for administrators and data scientists, and an assigned technical account manager (TAM) to maintain ongoing team meetings, identify and triage escalations, and keep users informed about product roadmap changes.Training and Consulting
Anaconda Enterprise offers various training and consulting services to help users maximize their investment. These include half-day live training sessions for Anaconda Enterprise administrators and data scientists, delivered via web conference. Additional training seats, in-person training, and professional services are available at an extra cost.Resource Management and Configuration
For administrative tasks, Anaconda Enterprise allows users to customize resource profiles to allocate compute resources such as memory, cores, and GPUs. Administrators can set up these profiles through the Administrative Console, ensuring that users have the necessary resources for their projects. This includes creating custom resource profiles for nodes with specific capabilities, such as GPUs, to accelerate computations.Package Management and Security
Anaconda Enterprise includes a repository feature that allows organizations to store, retrieve, and share open-source and proprietary packages securely. This repository can be installed behind an enterprise firewall or in an air-gapped environment, supporting various package repositories like PyPI, conda, and the Anaconda Repository. The platform also provides tools for scanning and verifying packages to ensure secure package management.Community and Documentation
Anaconda has a strong community of users and contributors, offering extensive documentation, tutorials, and forums for support. Users can access these resources to seek help and share knowledge. The platform also encourages feedback and feature requests, allowing users to submit support tickets or use a form to request new features.Reporting Issues and Feature Requests
If users encounter issues, they can report bugs or request new features through the support system. Administrators can open support tickets on behalf of users, and there are specific forms available for submitting feature requests and reporting bugs. Overall, Anaconda Enterprise provides a well-rounded support system that includes various levels of technical support, training opportunities, and resources for managing and securing data science environments. Anaconda Enterprise - Pros and Cons
Advantages of Anaconda Enterprise
Anaconda Enterprise offers several significant advantages that make it a powerful tool in the data science and AI-driven product category:Comprehensive Tool Suite
Anaconda Enterprise provides a wide array of tools and resources, including data processing, analytics, machine learning, and visualization capabilities. This comprehensive suite ensures that every aspect of AI implementation is covered, facilitating seamless integration into existing workflows.Scalability and Flexibility
The platform is highly scalable, allowing users to quickly provision their data science projects and applications with the necessary computational resources. It supports deployment in both on-premises and cloud-based environments, such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud.Collaboration and Team Management
Anaconda Enterprise features strong collaboration tools, enabling team members to share data science projects, edit them in real time, and access resources like machine-learning models, online notebooks, and dashboards. This helps avoid duplication of efforts and misalignment of priorities.Security and Governance
The platform implements robust security measures, including TTL or SSL encryption, to protect against security breaches and data leakage. It also allows for the management and governance of data science models, versions, and packages, ensuring compliance with regulatory standards.Reproducibility and Version Control
Anaconda Enterprise ensures reproducibility by tracking and logging changes, allowing users to maintain a history of project evolution and revert to previous states if necessary. This ensures consistency and reliability in data science projects.User-Friendly and Accessible
The platform is known for its user-friendliness, providing tools like Jupyter Notebooks, Jupyter Lab, and Spyder, which make it easier for users to work continuously within the environment without switching between programs.Community and Resources
Anaconda Enterprise benefits from a large and active community of users and developers, providing a wealth of resources, tutorials, and forums to help users get the most out of the platform.Disadvantages of Anaconda Enterprise
While Anaconda Enterprise offers many advantages, there are also some notable disadvantages to consider:Large Size and Slow Performance
Anaconda comes with a large number of pre-installed packages, which can make the installation file large and slow to download and install. This can also take up significant storage space and potentially slow down computer performance, especially during resource-intensive tasks.Compatibility Issues
Some packages included in Anaconda may not be compatible with certain versions of Python or other packages, leading to conflicts and difficulties in running certain scripts or projects.Limited Control Over Packages
The predefined set of packages in Anaconda can limit users’ control over the installed packages and versions, making it difficult to customize the distribution for specific needs.Limited Package Availability
Anaconda may not include the latest versions of certain packages or may lack specific packages required for a project. This can necessitate additional setup steps or manual package installations.Dependency on the System
Users need to trust the system with all their in-progress projects, which can be risky if the system crashes or fails, potentially leading to project loss. By understanding these pros and cons, users can make informed decisions about whether Anaconda Enterprise is the right fit for their data science and AI needs. Anaconda Enterprise - Comparison with Competitors
When comparing Anaconda Enterprise with other AI-driven data tools in its category, several unique features and potential alternatives stand out.
Unique Features of Anaconda Enterprise
Anaconda Enterprise is distinguished by its comprehensive and scalable architecture, which allows teams of data scientists to create, supervise, and automate AI-powered data science models and pipelines across various environments, including on-premises and cloud-based setups.
- Collaboration and Real-Time Sharing: Anaconda Enterprise offers robust collaboration features, enabling team members to share and edit data science projects in real time. It integrates with Jupyter Notebooks and JupyterLab, facilitating collaborative work on web-based documents that include live codes, data visualizations, and narrative texts.
- Scalable Architecture: The platform can automatically scale up or down depending on the computational resources needed, leveraging big-data frameworks like Hadoop and Apache Spark. This scalability ensures that users can quickly provision their data science projects with the necessary resources.
- Governance and Security: Anaconda Enterprise provides strong governance features, including an online repository for managing data science packages and versions, license management, auditing, and reporting. It also uses token-based access and SSL encryption to ensure secure collaboration and data protection.
- Self-Service Deployment: Users can deploy AI-powered data science models, dashboards, and browser-based notebooks with full flexibility and control, both locally and remotely to Hadoop or Apache Spark server clusters, as well as to cloud services like AWS, Azure, and Google Cloud.
Potential Alternatives
Tableau
Tableau is a leading business intelligence platform known for its advanced visualizations and AI capabilities. It integrates AI tools like Tableau GPT and Tableau Pulse to enhance data analysis, preparation, and governance. However, it can be challenging for new users or those without extensive data experience.
- Pros: Advanced visualizations, intuitive drag-and-drop interface, seamless integration with Salesforce data.
- Cons: Steeper learning curve for new users.
IBM Cognos Analytics
IBM Cognos Analytics is an integrated self-service solution that leverages AI-powered automation and insights. It offers automated pattern detection, natural language query support, and advanced analytics capabilities. However, it is known for its complex interface and high cost, making it less accessible to small to mid-sized companies.
- Pros: Integrates with IBM tools and IBM Watson, supports natural language inquiries.
- Cons: Complex interface, high cost.
Power BI
Power BI by Microsoft leverages AI to automate data preparation and provide insights through natural language queries. It is highly integrated with Microsoft products, making it a favorite among business analysts. However, it may not offer the same level of scalability and governance as Anaconda Enterprise.
- Pros: Integration with Microsoft products, real-time data insights.
- Cons: May lack the advanced governance and scalability features of Anaconda Enterprise.
Qlik
Qlik offers multiple data exploration features and a user-friendly interface but is ranked lower due to its higher cost and limited AI functionalities compared to competitors. It has an associative data model for flexible data exploration but may not match the comprehensive AI and governance features of Anaconda Enterprise.
- Pros: Associative data model, collaborative tools.
- Cons: Higher cost, limited AI features.
Other Tools
Other tools like Alteryx, Trifacta, and RapidMiner focus more on specific aspects of data analytics such as data preparation, blending, and machine learning. While they are powerful in their respective domains, they do not offer the same breadth of features and scalability as Anaconda Enterprise.
- Alteryx: Excels in data preparation and blending with a drag-and-drop interface.
- Trifacta: Specializes in data wrangling and preparation.
- RapidMiner: Offers a comprehensive data science platform with a focus on machine learning and a user-friendly interface.
In summary, Anaconda Enterprise stands out for its comprehensive suite of tools, scalable architecture, and strong governance and security features, making it a robust choice for enterprises looking to manage and deploy AI-powered data science projects. While other tools like Tableau, IBM Cognos Analytics, and Power BI offer strong AI-driven analytics capabilities, they may lack the scalability, governance, and collaboration features that Anaconda Enterprise provides.
Anaconda Enterprise - Frequently Asked Questions
Frequently Asked Questions about Anaconda Enterprise
Can I deploy multiple data science applications to Anaconda Enterprise?
Yes, you can deploy multiple data science applications and languages across an Anaconda Enterprise cluster. Each application runs in a secure and isolated environment with all the necessary dependencies from Anaconda. A single node can run multiple applications based on the available compute resources (CPU and RAM).
Does Anaconda Enterprise support high availability deployments?
Partially, Anaconda Enterprise supports high availability deployments. Some services and user-deployed apps are automatically configured for fault tolerance and service continuity when installed on three or more nodes. This includes automatic restarts, health checks, and service migration.
What types of projects can be deployed on Anaconda Enterprise?
Anaconda Enterprise can deploy a variety of projects, including:
- Notebooks (Python and R)
- Bokeh applications and dashboards
- REST APIs in Python and R (including machine learning scoring and predictions)
- Python and R scripts
- Third-party apps, web frameworks, and visualization tools such as Tensorboard, Flask, Falcon, deck.gl, and plot.ly Dash.
Does Anaconda Enterprise include Docker images for my data science projects?
Anaconda Enterprise does not explicitly mention including Docker images for user projects, but it does provide sample projects that include examples with various libraries like Bokeh, pandas, scipy, Shiny, Tensorflow, and xgboost. These sample projects can be saved to your Anaconda Enterprise account or downloaded to your local machine.
Does Anaconda Enterprise support batch scoring with REST APIs?
Yes, Anaconda Enterprise can be used to deploy machine learning models with REST APIs (in Python and R) that can be queried for batch scoring workflows. These REST APIs can be made available to other users and accessed with an API token.
How does Anaconda Enterprise handle collaboration and real-time editing?
Anaconda Enterprise is equipped with collaboration features that enable team members to share data science projects and edit them in real time. The platform allows teams to share resources such as machine-learning models, online notebooks, dependencies, and dashboards, ensuring that all team members have access to the necessary resources.
What security features does Anaconda Enterprise offer?
Anaconda Enterprise implements several security features, including TTL or SSL encryption to secure team collaboration. It also uses a token-based system for accessing deployed data science models and applications, ensuring that sensitive data is not exposed. Additionally, the platform generates logs to record activities and events related to data science projects, packages, and deployments.
Does Anaconda Enterprise provide tools to help define and implement REST APIs?
Yes, Anaconda Enterprise includes tools to help define and implement REST APIs. It provides an API wrapper for Python frameworks that builds on top of existing web frameworks, making it easy to expose existing data science models with minimal code. Users can also deploy REST APIs using existing API frameworks for Python and R.
What are the pricing options for Anaconda Enterprise?
Anaconda Enterprise offers several pricing tiers:
- Free Tier: $0 per month, with limited features.
- Starter Tier: $9 per month, per user.
- Business Tier: $50 per month, per user.
- Enterprise Tier: $60 per month, per user, with additional features and support.
Does Anaconda Enterprise offer support and training?
Yes, Anaconda Enterprise offers full support and training. Users can access phone and live chat support with the paid tiers. Additionally, the platform provides product training for collaborative, end-to-end data science workflows.
How does Anaconda Enterprise manage data science packages, versions, and tools?
Anaconda Enterprise makes it easy to govern data science projects, models, and resources. It has an online repository for storing on-premises data science packages and stacks. The platform allows users to control the tools, packages, and versions being utilized by their teams and generates reports for auditing licenses.