Data Science and AI Capabilities from Anaconda

Air-gapped Environment

Isolate your platform’s computing infrastructure from external networks, including the internet, for security.

Anaconda Assistant

Chat with an AI-powered assistant directly in your notebook for help with coding, generating plots, describing dataframes, debugging errors, and more.

Application Deployments

Panel, an open-source Python tool developed and maintained by Anaconda, seamlessly integrates with Anaconda’s Cloud Notebook service to allow one-click deployment and easy sharing.

Dashboards

Effortlessly deploy and share captivating, interactive dashboards using Panel.

Data Management

All-in-one data management solution, integrating acquisition, analysis, and collaboration through a comprehensive suite of tools and libraries.

Deploy Inference APIs

Make coding in Python easier with a suite of tools from Anaconda. Complex data science models and machine learning algorithms can integrate with software applications, web services, and IT infrastructure for a streamlined approach.

Easy Deployment

Take advantage of one-click deployment using the Panel app to quickly share models with stakeholders for easy insights and real-time reporting. 

Error Tracking and Logging

Effectively identify, diagnose, and resolve errors in data science projects. Enable continuous improvement practices to create a robust, reproducible, and auditable data science pipeline. 

GenAI

Leverage advanced ML algorithms to create business value using generative AI.

Governance

Establish clear policies and controls in your data science and AI projects. 

Machine Learning

Develop, train, evaluate, and deploy machine learning models. Streamline with scalable resources and integrated tools.

MLOps

Bridge the gap between your Data Science and IT teams. Operationalize machine learning workflows and drive value from your AI initiatives.

Model Library

Safely leverage pre-built models in our repository. The centralized hub for storing, sharing, and discovering pre-built models

On-Demand Infrastructure

Manage data-science sources efficiently with this performant infrastructure. Its scalable, cost- efficient, accessible and extremely fast to setup.

Reproducibility

Ensure data science and AI projects are not just innovative but also reproducible. Easily track and log changes and version control to ensure your team can iterate and build quickly. 

Sample Projects

Unlock data science excellence with Workbench sample projects and ready-to-use models.

Secure Package Management

Implement robust security tools and practices to protect your organization while keeping you on the forefront of technological solutions through the use of open-source software.

Team Collaboration

Avoid duplication of efforts and misalignment of priorities. Leverage expertise, share knowledge, and maintain organized data science workflows.

Version Controls

Maintain a history of changes, allowing users to track the evolution of projects and revert to previous states if necessary to ensure consistency.

Visualizations

Derive insights from intricate datasets using 

comprehensive visualization libraries, Jupyter Notebooks integration, dashboarding solutions, and scalability and performance optimizations.