Building AI solutions involves critical strategic choices impacting cost, performance, and time-to-market. GenAI use cases and associated compute need has grown exponentially in the last 2-3 years. AI developers have been posed with decisions on how to architect their applications selecting between models, training, fine-tuning, and inference techniques, managed vs. self-managed options to building AI models and applications. This session offers a pragmatic framework for navigating those choices. We'll demystify AI Infrastructure selection and deployment approaches, empowering you to make informed decisions that align your AI initiatives directly with business objectives.
Diwakar Bansal
Ruchi Bhatia
Ruchi Bhatia is a Computer Engineer with a Master’s degree in Information Systems Management from Carnegie Mellon University. She currently serves as a Product Marketing Manager at Amazon Web Services, driving awareness, engagement, and adoption of AI infrastructure solutions including Trainium and Inferentia. Ruchi is proud to be the youngest triple Kaggle Grandmaster across the Notebooks, Datasets, and Discussion categories. In her previous role as Product Marketing Manager at HP, she focused on data science and AI solutions, further strengthening her expertise at the intersection of technology and business. She also served as a Global Data Science Ambassador for Z by HP, sharing her knowledge with the broader data science community. Earlier in her career, Ruchi worked across the technology divisions of major global firms, gaining valuable industry perspective and experience. As Leader of Data Science at OpenMined, she guided a team of data scientists in developing innovative privacy-preserving AI solutions. Her expertise has led to speaking invitations at Google Cloud Next, NVIDIA GTC, and the University of Oxford, where she shares insights on AI and data science applications.