How Much Open Source Is TOO MUCH OPEN SOURCE?

Intel's OpenVINO toolkit helps developers by streamlining code writing, freeing them to concentrate on other vital project aspects. AI Evangelist at Intel, Anisha Udayakumar, elucidates on OpenVINO's versatility

- ANISHA UDAYAKUMAR

As its name implies, open source knows no bounds and extends even into proprietary hardware companies. Intel’s use of open source for model optimisation showcases its ability to nurture a wide array of products in the rapidly expanding field of artificial intelligence and machine learning. This approach has not only showcased the potential of open source but has also established a brilliant business model within a proprietary ecosystem.

With its open visual inference and neural network optimisation (OpenVINO) toolkit, tailored for developers, Intel trains computers to recognise patterns, quickly and accurately. Initially aimed at the computer vision market, this open sourced toolkit has expanded its versatility to optimise models to understand language or generate content (text or images) through artificial intelligence across various domains, including NLP, generative AI, and emerging large language models.

There is an abundance of open source toolkits in the field of deep learning and neural network optimisation. Whether it’s Facebook’s PyTorch for research, Keras for user-friendly applications, or Apache’s MXNet for unparalleled scalability, the question arises: What sets OpenVINO apart?