GPU’s for AI Startup

GPU’s for AI Startups

Artificial Intelligence (AI) has been evolving at a higher pace. It is poised to become one of the most impactful technologies humanity has ever created, boosting us and giving the ability to solve the problems once thought to be unsolvable! 

Intelligent AI systems are being built having innovative algorithms & models that predict and prescribe the outcomes accurately, which are powered by essential a) underlying HPC compute platform, b) computes ability to interface, and self-tune at device level, c) associate & bind necessary built-in multi matrix operations, and d) effectively tweaking the runtime environments on the fly, for the desired outcome. GPU’s and its closely coupled platform plays a key role in building the smartest AI systems and successful AI Startups!

Typical journey of an AI model to evolve from Base model to Personalized model for an AI Startup and GPU Usage –

	Base Models	Industry Specific Model	Customer Specific Model	Personalized Model
Objective of the models	Typically a generalized model for many industries	Target Industry & similar dataset for the model to be built upon	Specific is terrific! Target Customer & the dataset from Customer to acquent the model on customer specific data	Loopback after inferencing for retraining the models to capture personalized preferences
Examples	Image Recognition; Sentiment Analysis; Speech to text etc.	Speech to text model trained for Indian accent and its ability to decode Indian Automobile Sector terms	Speech to text model trained on customer specific automobile components, policies & offers	Retail Industry would expect the personalized preference bing generated while making purchasing decision and would be interested in capturing it for awesome customer experience
Pros	Provides Jumpstart to AI Development	Target the specific Industry model where the problem lies	Customer specific trained model with 95% accuracy	Enhanced accuracy and hitting business objectives
Dataset	Diverse publicly available dataset	Maximum proportion of the Industry Specific Data in the base data to be trained upon e.g. Indian Automobile Sector in this case	More than 65% of dataset from the customer specific data	Data source could be from Social media, web history, click preferences, transactional data etc.
Expected Accuracy	70% to 80%	80% to 85%	85% to 95%	95% to 100%
Assumption on accuracy	Human accuracy levels are assumed to be between 90% to 95%
% of execution for final model	40%	60%	80%	100%
Usage of GPU	Occasional	Continuous for Training	Continuous for Training	Continuous for Training & Inference
Where should the GPU be?	GPU on Cloud	GPU on Cloud	GPU on Cloud	In-house or GPU on Cloud

 

While GPUs are equally important, its also essential to have the GPU platform associated with the underlying key components fulfilling all the dependencies in order to expedite AI Startup Journey towards success. Specifically if the AI Startups are empowered with the GPU’s and the platform with below features –

Key GPU & associated platform components for a successful AI startup

• Instant access to the industry datasets from various repositories such as UCI, Kaggle & Research Institutes. 
• Access to set of well-known ML algorithms to jumpstart the AI model development
• Easy access to auto detect the relevant model applicable to the dataset of your interest
• Platform to link the trained model to the end-customer through globally accepted protocols such as REST & RPC
• Customized & AI based DevOps to manage the AI model lifecycle
• And finally collaboration options to share the cost & simplified pricing through the AI model lifecycle

Concluding Remarks

Selecting appropriate mix of hardware platform, software mix, frameworks, access to the datasets, models, pipe-line, along with simplified pricing for base models, industry models, customer model, and end-user models is the key to being a successful AI startup.