Building AI Solutions with NVIDIA
The following article is based on an online workshop conducted by the E2E - one of the largest cloud service providers in India; in collaboration with NVIDIA. The aim of this workshop is to serve as a guide for building AI solutions on NVIDIA accelerated computing platform. The primary agenda is to expound on upcoming AI-based avenues of Conversational AI, Recommender Systems, Healthcare and Lifesciences, Intelligent Video Analytics, etc. The panel further discusses building AI solutions by leveraging NVIDIA NGC and NVIDIA A100 TENSOR CORE GPU over E2E cloud.
About the Panel
The workshop has been moderated by Mr UnniKrishnan AR. He is the Head Developer, Ecosystem, at NVIDIA Deep Learning Institute and CSP Business, South Asia. The panel for the workshop also consists of Mr Ashish Sardana(Data Scientist, NVIDIA), Ms Pooja Venkatesh(Developer Relations Manager, IVA-NVIDIA) and Mr Pallab Maji(Senior Solutions Architect Deep Learning, NVIDIA).
Introduction to NVIDIA NGC Catalog
NVIDIA NGC is a software hub that contains the latest software tools designed to assist developers and infrastructure maintenance teams responsible for building an organisation’s software stack. Mr. Pallab gives an overview of the NVIDIA NGC as “a software hub that houses different artefacts required for algorithm development”. These artefacts available on NGC play a vital role in developing algorithms for machine learning, deep learning, and high-performance computing ecosystems. Developers can increase their efficiency multifold by availing all the best software development resources located at one place, rather than having to wander around the internet for the same. Interested individuals can register themselves here and browse through all of NGC’s offerings.
Conversational AI
“Language is a medium of communication and its use for human activities is obvious”, states Mr UnniKrishnan, as the workshop moves towards the topic of Conversational AI. NVIDIA has developed a full-fledged stack for supporting conversational AI that can be utilised by a wide array of users ranging from enterprises needing it for their internal application purposes to solution-providers looking to plug a conversational AI piece into their products. Mr Pallab furthers the discussion saying, “Currently, it is one of the best times to work on Conversational AI”. He backs his statement by explaining how there has been a multitude of innovations in speech recognition and language modelling technologies, both of which are pivotal in improving the accuracy of Conversational AI. The key here is to combine visual and speech cues accurately, and then to be able to deliver an appropriate response while maintaining the quality of service. The NVIDIA JARVIS module is a prime example of this, as it provides multi-modal conversational AI skills while delivering low-latency. Transcribing multi-user, multi-context conversations, and End-to-end conversational AI systems are two of many applications of the JARVIS model.
Recommender Systems
The moderator starts the next segment of the workshop with the following assertion, “Just like the Conversational AI, the most critical piece out there in the present age of AI is the personalisation of the Internet”. Mr Ashish Sardana explains how the recommenders will help make this possible. According to a report from McKinsey, “Already, 35% of what consumers purchase on Amazon and 75% of what they watch on Netflix come from product recommendations based on such algorithms”. It suggests that recommender systems help internet provider companies personalise the internet for users. Industries like E-commerce, digital content, etc. are heavily dependent on recommender systems to connect with their consumers. NVIDIA’s MERLIN is a framework for developing recommender systems that can be employed by developers for the needs of their respective organisations.
Healthcare and Lifesciences
Mr UnniKrishnan takes up the topic of Healthcare and Lifesciences for the penultimate section of the workshop. According to him, “The value proposition of AI and combination of AI with HPC towards the healthcare is phenomenal”. Mr Sardana covers this topic as well; elaborating the categorisation of CLARA framework into CLARA Guardian, CLARA Discovery and CLARA for Medical Imaging. Most of NVIDIA’s work has been focused on the medical imaging aspect, which also happens to be the healthcare industry’s facet with most acceptance for AI. However, the present pandemic situation has put a great impetus on CLARA Guardian and CLARA Discovery as well. The former has tremendous applications in developing smart and contactless hospitals, which is the need of the hour. CLARA Discovery, on the other hand, is focussed on combinational genomics that is used for developing vaccines and drugs.
Intelligent Video Analytics
In the final segment of the workshop, Ms Pooja elaborates on the work NVIDIA has been doing in Intelligent Video Analytics. According to her, “The IVA market today is a two trillion dollar industry”. The purpose of implementing IVA is to gain predictive insights from the huge amount of video data that is being generated globally. These insights can be used across a plethora of industry segments like Traffic management, Warehouses & Logistics, Access control, etc. NVIDIA’s METROPOLIS stack is a software development kit which can be used at high levels of scalability across the above-mentioned industry applications.
The moderator conducted a brief Q&A session at the end to revisit all of the above topics, before wrapping things up.
