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

在越来越多的数据驱动的世界和商业成本不断增加的背景下，人工智能和机器学习在帮助企业获得商业优势（增加销售额，提高投资回报率和降低运营成本）方面至关重要。机器学习的实施主要通过“框架”（用于构建/部署/扩展模型的工具/库的软件接口）来实现。这样的框架被“锁定”到它们的提供者中，并且与其他框架不兼容，从而阻止了在不同框架中编写的库/模型的简单使用。这从根本上降低了可用性和对不断发布的大量ML开发的访问，或者需要耗时/昂贵/容易出错的手动代码转录。（“Ivy-SST”）通过统一ML框架来克服这种互操作性的缺乏，以使任何ML代码能够在任何硬件上的任何框架的任何版本上运行，并通过输出人类可读的代码，在选择的框架内允许进一步的操作/开发/细化。通过这样做，我们将帮助企业更快/更便宜/更有效地实现ML实现，以获得可衡量的商业利益：收入增加（+11.5%），运营成本降低（-30%），废物减少（-20%），以提高可持续性/效率。

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.