Morph
Overview of Morph
Morph: Build AI-powered Data Apps with Code
Morph is a Python framework + hosting platform that allows you to build and deploy AI-powered data applications quickly and securely. It offers built-in authentication, data connectors, CI/CD, and RBAC.
What is Morph?
Morph is a platform that allows you to build internal AI applications fast, securely, and with high performance. It combines a Python framework with hosting, enabling you to connect to business data, build data processing using OpenAI API and ML models, and create interactive screens using Markdown.
How to use Morph?
Install the Morph Python framework:
$ pip install morph-dataStart building your app using Python and React packages.
Deploy your app on a secure server provided by Morph.
Why is Morph important?
Morph simplifies the process of building and deploying data applications, providing a secure environment with role-based access control. It offers flexibility by allowing you to integrate various Python and React libraries.
Key Features:
- Python Framework: Use the power of the Python ecosystem to process your data.
- Customizable AI: Build custom AI processes using the LLM API.
- Pre-made Components: Easily create interactive AI apps with pre-made components or write your own in Markdown.
- Secure Deployment: Deploy your app in a secure environment accessible only to your team.
- Data Connectors: Securely connect to internal data from sources like BigQuery, Snowflake, PostgreSQL, Salesforce, Intercom, and Linear.
- Role-Based Access Control: Manage permissions with built-in role settings.
- Data Governance: Monitor data lineage with a visual flow of SQL and Python processes.
- Git Management: Manage code as a Git repository.
- Scheduled Execution: Automate SQL and Python processes with scheduled runs.
Pricing:
Morph offers different pricing plans to suit different team sizes and needs:
- Free: For early-stage data teams (up to 1 user, 5 projects).
- Pro: For professional data teams (includes team collaboration, more projects, and local LLM deployment with Ollama).
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