Shankar Krishnan
AWS, Product Manager - AI/ML
Boston, Massachusetts, United States
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I bring 15+ years of AI/ML experience, with the last five years focused specifically on voice AI at AWS, where I led product development within the Amazon Bedrock GenAI organization for conversational AI. In that role, I managed a team of 30+ ML scientists and collaborated with 100+ engineers to launch and scale multiple 0-to-1 ML products. These products are in production at companies including Slack, Intuit, Zillow, Oracle, and T-Mobile.
Prior to AWS, I led conversational AI initiatives for IBM Watson serving large enterprise clients, and held AI product leadership roles at Aptiv (autonomous vehicles) and Upserve (AI analytics for SMBs). I currently serve as GenAI Product Leader at Stripe.
Additionally, I have spoken at several conferences (e.g., Optimized AI and MLCON) and written articles for several magazines including Forbes, and TechTimes, among others.
Area of Expertise
Topics
Using AI to improve productivity of doctors
Provide an overview of how GenAI can be used to improve productivity of doctors and extract useful insights from a doctor:patient conversation that can be also used to improve patient outcomes.
Using AI to improve productivity of doctors
This talk will provide an overview of best practices in designing and scaling both synchronous and asynchronous machine learning (ML) APIs. This includes optimizing the APIs to deliver the best performance from a latency, accuracy, and cost perspective. The talk will focus on key checklist items before the handoff between from ML teams to engineering, scaling in production including infrastructure management, monitoring/troubleshooting of issues, and ML OPS. Lastly, key learnings from updating ML APIs in production while minimizing user impact will be shared
Using AI to improve productivity of doctors
This talk will provide an overview of best practices in designing and scaling both synchronous and asynchronous machine learning (ML) APIs. This includes optimizing the APIs to deliver the best performance from a latency, accuracy, and cost perspective. The talk will focus on key checklist items before the handoff between from ML teams to engineering, scaling in production including infrastructure management, monitoring/troubleshooting of issues, and ML OPS. Lastly, key learnings from updating ML APIs in production while minimizing user impact will be shared
Shankar krishnan
Provide strategies add Generative AI into products, roadmap execution, benefits to end customers
Best practices in building and scaling ML APIs
This talk will provide an overview of best practices in designing and scaling both synchronous and asynchronous machine learning (ML) APIs. This includes optimizing the APIs to deliver the best performance from a latency, accuracy, and cost perspective. The talk will focus on key checklist items before the handoff between from ML teams to engineering, scaling in production including infrastructure management, monitoring/troubleshooting of issues, and ML OPS. Lastly, key learnings from updating ML APIs in production while minimizing user impact will be shared
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