An artificial intelligence framework allows for easier and faster creation of AI applications. These include machine learning, deep learning, neural networks, and NLP (natural language processing) solutions.

Why you need an AI framework

Artificial intelligence (AI) has been becoming more mainstream slowly over time. Companies have been amassing large amounts of data, referred to as “big data,” and have therefore grown the need for technologies that are powerful enough to analyze and leverage that data. This is why most technologies today require an AI framework foundation.


AI is changing business enterprises

AI is changing business in many ways, including marketing, finance, product development, customer service, and more. Since artificial intelligence is such a large investment, enterprises have an advantage over smaller companies since they can get in the machine learning and AI game. ML is a competitive advantage since it allows opportunities for competitive advantages to be found through robust and complex data analysis.


What is an AI framework?

An AI framework allows for easier and faster creation of artificial intelligence applications. These include machine learning, deep learning, neural networks, and NLP (natural language processing) solutions.

What is an AI framework?

AI frameworks act as a template for the enterprise artificial intelligence system as a whole. This makes development, deployment, and governance easier and seamlessly integrated together. Any potential security breaches or quality control issues that could happen along the way are eradicated when applications exist in a solid AI framework. 


Features of Algorithmia’s AI framework

Algorithmia’s AI framework allows enterprises to deploy, manage, and scale their artificial intelligence portfolio. Algorithmia is the fastest route to deployment, making it easy to securely govern artificial intelligence operations with a healthy AI lifecycle.

With Algorithmia, you can connect your data and pre-trained AI models, deploy and serve as APIs, manage your models and monitor performance, and secure your artificial intelligence portfolio as it scales.

AI Framework Features


A flexible AI framework connects to all necessary data sources in one secure, central location for reusable, repeatable, and collaborative AI model management. 

  • Manage source code by pushing AI models into production directly from the code repository
  • Control data access by running AI models close to connectors and data sources for optimal security
  • Deploy models from wherever they are with seamless AI framework and infrastructure management


Artificial intelligence models only achieve value once they reach production. Efficient deployment capabilities reduce the time it takes your organization to get a return on your AI investment. 

  • Deploy in any language and any format with flexible tooling capabilities 
  • Serve AI models with a git push to a highly scalable API in seconds
  • Version models automatically with an AI framework that compares and updates models while maintaining a dependable version for calls.


Manage AIOps using access controls and governance features that secure and audit the artificial intelligence models you have in production. 

  • Split artificial intelligence workflows into reusable, independent parts and pipeline them together with a microservices architecture
  • Operate your AI portfolio from one, secure location to prevent work silos with a robust AI framework management system
  • Protect your AI models with access control
  • Usage reporting allows you to gain full visibility into server use, model consumption, and call details to control costs


A properly scaled artificial intelligence lifecycle scales on demand, operates at peak performance, and continuously delivers value from one AIOps center.

  • Serverless scaling allows you to scale models on demand without latency concerns, providing CPU and GPU support 
  • Reduce data security vulnerabilities by access controlling your AI model management system
  • Govern models and test model performance for speed, accuracy, and drift
  • Multi-cloud flexibility provides the options to deploy on Algorithmia, the cloud, or on-prem to keep models near data sources