AWS re:Think Podcast

• Episode 41: Rethinking Data Strategy for Healthcare and Life Sciences with TileDB

  In this episode, we explore how TileDB, an innovative data system originally developed at MIT and Intel Labs, is revolutionizing healthcare and life sciences data management. We'll dive deep into how TileDB's universal data engine efficiently handles complex scientific data - from genomic sequences to medical imaging - in a unified format, and learn how healthcare organizations can leverage TileDB on AWS to accelerate research, improve collaboration, and reduce infrastructure costs. Join host AWS Solutions Architect for HCLS Gokhul Srinivasan and guest Devika Garg form TileDB as they discuss real-world applications in precision medicine, genomic analysis, and how TileDB is transforming the way healthcare providers manage and analyze patient data.To Learn More:https://www.tiledb.com/AWS Hosts: Nolan Chen & Gokhul SrinivasanEmail Your Feedback: [email protected]

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• Episode 40: AI Observabilty and Evaluation with Arize AI

  AI can still sometimes hallucinate and give less than optimal answers. To address this, we are joined by Arize AI’s Co-Founder a Aparna Dhinakaran for a discussion on Observability and Evaluation for AI. We begin by discussing the challenges AI Observability and Evaluation. For example, how does “LLM as a Judge” work? We conclude with some valuable advice from Aparna for first time entrepreneurs.Begin Observing and Evaluating your AI Applications with Open Source Phoenix:https://phoenix.arize.com/AWS Hosts: Nolan Chen & Malini ChatterjeeEmail Your Feedback: [email protected]

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• Episode 39: Rethink your AI Agents with MCP

  In this episode we are going to discuss Model Context Protocol (MCP) in context of Agentic architecture with Mona - Gen AI Specialist Solutions Architect at AWS.We will cover the challenges of implementing Agents and how MCP can help. We will try to simplify it with an example of MCP with Agents and further delve deep into LangGraph adapters such as FastMCP and FastAPI.You will also learn about implementing MCP architecture on AWS.Apart from her daily job, Mona has published two books Natural Language Processing with AWS AI Services: Derive strategic insights from unstructured data with Amazon Textract and Amazon Comprehend and Google Cloud Certified Professional Machine Learning Study Guide. She has authored multiple on AI/ML and cloud technology and a co-author on a research paper on CORD19 Neural Search. She is also a frequent speaker at multiple conferences such as ISMB 2022, AWS Re:Invent. Links https://www.amazon.com/Natural-Language-Processing-AWS-Services/dp/1801812535https://a.co/d/7rlkZwnBlog - Implement MCP in SageMaker AI https://aws.amazon.com/blogs/machine-learning/extend-large-language-models-powered-by-amazon-sagemaker-ai-using-model-context-protocol/Blog - Implement MCP with Bedrock Agents https://aws.amazon.com/blogs/machine-learning/harness-the-power-of-mcp-servers-with-amazon-bedrock-agents/AWS Hosts: Nolan Chen & Malini ChatterjeeEmail Your Feedback: [email protected]

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• Episode 38: AI Native Databases with Weaviate

  Join Jobi George and Tony Le from Weaviate as we discuss how their AI Native Vector Database is powering modern AI applications. We begin with an intro to Vector Databases followed by common use cases, and how they fit into Agentic Workflows. We also talk about the advantages of using an AI Native Database from the Open Source community. To learn more:Website - www.weaviate.ioLinkedin - https://www.linkedin.com/company/weaviate-io/AWS Marketplace - https://aws.amazon.com/marketplace/seller-profile?id=seller-jxgfug62rvpxsDeveloper Community - https://weaviate.io/developers/weaviate/model-providers/awsInstagram - https://www.instagram.com/weaviate.io/Youtube - https://www.youtube.com/@WeaviateAWS Hosts: Nolan Chen & Malini ChatterjeeEmail Your Feedback: [email protected]

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• Episode 37: AI Model Merging with Arcee AI

  Join Julien Simon, Chief Evangelist of Arcee AI, as we discuss how Model Merging can help us build more versatile and cost effective AI models. Hear him explain the different methods for Model Merging while comparing it with other techniques for building better models such as Model Distillation and Transfer Learning. To learn more about Model Merging:Arcee AI Websitehttps://arcee.aiArcee’s MergeKit: A Toolkit for Merging Large Language Modelshttps://arxiv.org/abs/2403.13257Deep dive: model merging on YouTube:https://youtu.be/cvOpX75Kz4M?si=nT2U7Fe8oLNRSy0uTry MergeKit:https://github.com/arcee-ai/mergekitAWS Hosts: Nolan Chen & Malini ChatterjeeEmail Your Feedback: [email protected]

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