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What is UnionML?
Creating machine learning applications should be a smooth and straightforward process. UnionML is a Python-based open-source framework that builds upon Flyte™, simplifying the complex world of ML tools into a unified interface. It allows you to easily incorporate your preferred tools through a simple and standardized API, minimizing boilerplate code so you can focus on what truly counts: the data and the models that yield valuable insights. This framework makes it easier to merge a wide variety of tools and frameworks into a single protocol for machine learning. Utilizing established industry practices, you can set up endpoints for data collection, model training, prediction serving, and much more—all within one cohesive ML system. Consequently, data scientists, ML engineers, and MLOps experts can work together seamlessly using UnionML applications, creating a clear reference point for comprehending the dynamics of your machine learning architecture. This collaborative environment not only encourages innovation but also improves communication among team members, significantly boosting the overall productivity and success of machine learning initiatives. Ultimately, UnionML serves as a vital asset for teams aiming to achieve greater agility and productivity in their ML endeavors.
What is FastMCP?
FastMCP is an innovative open-source framework built in Python that streamlines the development of Model Context Protocol (MCP) applications by simplifying the processes of creating, managing, and interacting with MCP servers, thereby allowing developers to focus on their essential business logic without getting bogged down by the intricacies of the protocol. The Model Context Protocol (MCP) provides a standardized approach for securely connecting large language models to various tools, data, and services, and FastMCP enhances this experience with a user-friendly API that minimizes boilerplate code through the use of Python decorators designed for registering tools, resources, and prompts. To establish a standard FastMCP server, one simply creates a FastMCP object, applies decorators to designate Python functions as tools that can be called by the LLM, and then activates the server using various built-in transport methods like stdio or HTTP; this configuration allows AI clients to interact with the code as though it were part of the model’s context. Furthermore, the architecture of FastMCP encourages efficient development practices, enabling teams to rapidly iterate on their projects while upholding superior standards of code quality and performance. This efficiency not only accelerates the development cycle but also enhances collaboration among team members, ensuring that everyone can contribute effectively to the project's advancement.
Integrations Supported
Python
Amazon Web Services (AWS)
Google Cloud Platform
Markdown
Microsoft Azure
Model Context Protocol (MCP)
Integrations Supported
Python
Amazon Web Services (AWS)
Google Cloud Platform
Markdown
Microsoft Azure
Model Context Protocol (MCP)
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
Free
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
Union
Date Founded
2021
Company Location
United States
Company Website
www.union.ai/unionml
Company Facts
Organization Name
fastmcp
Date Founded
2025
Company Location
United States
Company Website
gofastmcp.com/getting-started/welcome
Categories and Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization