What is JAI-SDK?#
JAI is the easiest way for developers to build, publish and integrate AI solutions
Made for developers of all levels of expertise – even if they aren’t data scientists
Scalable models up and running in minutes, not months
Works reliably with real-world, messy data
Allows easy integration across different technology stacks
JAI accomplishes this mission by:
Simplifying the complex, messy world of data science in three simple methods for building and integrating AI solutions.
Providing the necessary environment and processment power (e.g. GPU) to access powerful Deep Learning models, without requiring specific knowledge
Enabling end-users to conjugate multimodal data sources (text, images, and tabular) and build solutions even with little machine learning knowledge
Enabling data sharing without compromising confidentiality through meaningful vector sharing (a structure that encrypts the data without the possibility of reconstructing the original information)
No time lost in writing boilerplatecode.
Why Use JAI?
JAI simplifies ML-based solution lifecycle these tasks so that you can focus on building problem-solving strategies.
Developers can get started quicky (<1 min) and for free
JAI automates data conversion into machine format
Building new AI features leverages previous ones, leading to a much smaller time-to-production
JAI results are traceable and easier to debug
Model performance is robust to real-world data
Deploying AI into production is literally just one line of code away
JAI is more than a framework to build AI-based solutions but also an active community of developers, researchers, and folks that love machine learning. Here’s a list of tips for getting involved with the JAI community:
Check our Youtube videos
API and Package Reference#
- JAI Python Class
- Fit kwargs (keyword arguments)
- Utilities Functions
- Task Module Reference