14th June 2024
Amazon Web Services, Inc., a subsidiary of Amazon.com, Inc., has unveiled a $230m initiative aimed at supporting startups worldwide in developing generative AI applications.
This investment is designed to aid early-stage companies by providing AWS credits, mentorship, and education, thereby enhancing their capabilities in Artificial Intelligence and Machine Learning technologies.
As part of this commitment, AWS will fund the second cohort of its Generative AI Accelerator programme, the technology giant said in a statement posted on its website on Thursday.
This initiative offers hands-on expertise and up to $1m in credits to each of the top 80 early-stage startups leveraging generative AI to address complex challenges.
Applications for the AWS Generative AI Accelerator programme are now open and will be accepted until July 19, 2024.
The Vice-President, Artificial Intelligence Products at AWS, Matt Wood, said, “For more than 18 years, AWS has helped more startups build, launch, and scale their businesses than any other cloud provider—it’s no coincidence that 96 per cent of all AI and ML unicorns run on AWS.
“With this new effort, we will help startups launch and scale world-class businesses, providing the building blocks they need to unleash new AI applications that will impact all facets of how the world learns, connects, and does business.”
Startups can use AWS credits to access AWS compute, storage, and database technologies, as well as AWS Trainium and AWS Inferentia2, energy-efficient AI chips that offer high performance at the lowest cost.
These credits can also be used on Amazon SageMaker, a fully managed service that helps companies build and train their own FMs, as well as access models and tools to easily and securely build generative AI applications through Amazon Bedrock.
The AWS Generative AI Accelerator identifies top early-stage startups that are using generative AI to solve complex challenges in areas such as financial services, healthcare and life sciences, media and entertainment, business, and climate change, among others.
Participants will access sessions on ML performance enhancement, stack optimization, and go-to-market strategies. The 10-week program will match participants with both business and technical mentors based on industry verticals.
Startups will receive up to $1m each in AWS credits to help them build, train, test, and launch their generative AI solutions.
They will also have access to industry experts, technology, and technical sessions from NVIDIA, the program’s presenting partner, and be invited to join the NVIDIA Inception program, designed to nurture cutting-edge startups.
AWS will announce selected startups for the second cohort on September 10, and the programme will kick off on October 1 with in-person sessions at Amazon’s Seattle campus.
All 80 participating startups will be invited to attend and showcase their solutions to potential investors, customers, partners, and AWS leaders in December at re:Invent 2024 in Las Vegas.
The Co-founder and COO of Leonardo.AI, Jachin Bhasme, noted, “AWS has been instrumental in enabling us to scale our generative AI platform to meet the rapidly growing demand from our global user community.
Their robust generative AI infrastructure helped us reduce inferencing costs by 60 per cent and accelerate our language model inference speeds by up to 35 per cent.
“The accelerator was also an incredible experience for us. The business and technical mentorship we received and the connections we made played a crucial role in shaping our product and strategy,” Bhasme expounded.
The CEO and co-founder of Vevo Therapeutics, Nima Alidoust, said, “AWS has the right combination of technology, network of partners, and potential customers in the life sciences space that made it the best fit to support our vision of building the next generation of AI models for drug discovery.
“Participating in the AWS Generative AI Accelerator programme provided us with valuable opportunities to tap into this community and to leverage AWS’s infrastructure and expertise in scaling model training and development,” he remarked.
