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Description
Nitric is a versatile, open-source backend framework that operates across various cloud platforms, allowing developers to easily define their infrastructure through code and streamline deployment processes with customizable plugins. It is compatible with several programming languages, such as JavaScript, TypeScript, Python, Go, and Dart. Among its prominent features are the ability to create APIs (including REST and HTTP), serverless functions, routing, and handling authentication and authorization via OIDC. Additionally, it provides support for storage solutions, such as object and file storage, signed URLs, and bucket events, as well as database management, including managed Postgres with migration capabilities. Messaging features like queues, topics, and pub/sub are also integrated, alongside support for websockets, scheduled tasks, and managing sensitive information securely. Nitric can seamlessly integrate with infrastructure management tools like Terraform or Pulumi, or you can develop your own plugins, and it is compatible with leading cloud service providers like AWS, Azure, and Google Cloud. Furthermore, it offers a local development environment that simulates cloud conditions, enabling developers to prototype, test, and refine their applications without incurring any cloud expenses. The framework also places a strong emphasis on declarative security, efficient resource access management, and ensuring portability across different environments, making it a powerful choice for modern application development.
Description
Developing machine learning applications should be effortless and seamless. UnionML is an open-source framework in Python that enhances Flyte™, streamlining the intricate landscape of ML tools into a cohesive interface. You can integrate your favorite tools with a straightforward, standardized API, allowing you to reduce the amount of boilerplate code you write and concentrate on what truly matters: the data and the models that derive insights from it. This framework facilitates the integration of a diverse array of tools and frameworks into a unified protocol for machine learning. By employing industry-standard techniques, you can create endpoints for data retrieval, model training, prediction serving, and more—all within a single comprehensive ML stack. As a result, data scientists, ML engineers, and MLOps professionals can collaborate effectively using UnionML apps, establishing a definitive reference point for understanding the behavior of your machine learning system. This collaborative approach fosters innovation and streamlines communication among team members, ultimately enhancing the overall efficiency and effectiveness of ML projects.
API Access
Has API
Yes
API Access
Has API
Yes
Integrations
Amazon Web Services (AWS)
Yes
Google Cloud Platform
Yes
Microsoft Azure
Yes
Python
Yes
AWS Lambda
Yes
Dart
Yes
Docker
Yes
GitLab
Yes
Go
Yes
JavaScript
Yes
Integrations
Amazon Web Services (AWS)
Yes
Google Cloud Platform
Yes
Microsoft Azure
Yes
Python
Yes
AWS Lambda
No
Dart
No
Docker
No
GitLab
No
Go
No
JavaScript
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Nitric
Country
United States
Website
nitric.io
Vendor Details
Company Name
Union
Founded
2021
Country
United States
Website
www.union.ai/unionml
Product Features
Product Features
Machine Learning
Deep Learning
No
ML Algorithm Library
No
Model Training
No
Natural Language Processing (NLP)
No
Predictive Modeling
No
Statistical / Mathematical Tools
No
Templates
No
Visualization
No
