Unifiying Machine Learning (ML) Frameworks: Source-to-Source Transpilation of ML Code for Complete Interoperability of Frameworks, Versions and Hardware to Streamline Business ML Implementations for Revenue Increases and Operational Costs/Waste Reductions

In an increasingly data-driven world and against a backdrop of increasing business costs, AI and ML are of vital importance in helping businesses gain commercial advantage (increasing sales, higher returns on investment and reducing operating costs).

The implementation of ML is predominantly achieved with "frameworks" (software interfaces with tools/libraries for building/deploying/scaling models). Such frameworks are "locked" into their providers and are incompatible with others, preventing easy use of libraries/models written in different frameworks. This radically reduces usability and access to the wealth of ML developments constantly released, or requires time-consuming/costly/error-prone manual transcription of code.

Our innovative source-to-source transpiler ("Ivy-SST") overcomes this lack of interoperability by unifying ML frameworks to enable _any ML code_ to be run on _any version_ of _any framework_ on _any hardware_, and _permitting further manipulation/development/elaboration_ in the framework of choice by outputting _human-readable code_.

In doing so, we will help businesses achieve ML implementations faster/cheaper/more efficiently, for measurable business benefits: increased revenues (+11.5%), reduced operational costs (-30%) and waste reduction (-20%) for sustainability/efficiency improvements.