Your AI is only as good as the data its trained on. In this episode we talk to Krishna Subramaniam, Co-Founder and President of Komprise, a provider of intelligent Data Management. We discuss both the importance and challenge of unlocking the value unstructured data for AI pipelines. 

For more info on Komprise’s Intelligent Data Management Platform:

www.komprise.com

AWS Hosts: Nolan Chen & Malini Chatterjee

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"2025 is the year of inference." Todd Pond, AWS Director of Strategic Sales, is out in the field with commercial customers every day, helping them leverage technology to solve their biggest problems, accelerate innovation and transform their businesses with AI. He and his team have a deep understanding of these companies - the challenges they're up against, their limited resources, their unique value propositions, and how to enable their growth with maximum ROI. In this episode, we are happy to have Todd back again to discuss what were the 2024 trends, what were the pleasant surprises, his actionable insights and some recipe for success in 2025 and beyond. 

Join AWS Head of AI and Modern Data Strategy Business Development Ben Schreiner for a recap of re:Invent 2024. Ben share his top 5 themes from the conference as well as what surprised him in 2024 and what he thinks lies ahead in tech in 2025. 

AWS Hosts: Nolan Chen & Malini Chatterjee

Top Announcements from re:Invent 2024:

https://aws.amazon.com/blogs/aws/top-announcements-of-aws-reinvent-2024/

https://aws.amazon.com/blogs/machine-learning/aws-reinvent-2024-highlights-top-takeaways-from-swami-sivasubramanian-to-help-customers-manage-generative-ai-at-scale/

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#awsrethink

In this episode, Tailscale CEO Avery Pennarun joins us to discuss VPNs, network connectivity and security in the cloud era. We talk about hub & spoke vs full mesh networks as well current and future trends in networking. Avery also talks about how Tailscale’s approach to networking helps make setting up secure networks between devices and the cloud easier than before. 

For more info on Tailscale check out:

tailscale.com

AWS Hosts: Nolan Chen & Malini Chatterjee

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In this AWS re:Think Podcast episode, Rahul Bhargava reveals how Crop.photo by Evolphin is revolutionizing e-commerce visuals with AI magic. Discover how smart cropping and background generation are transforming product images from mundane to magnificent. Rahul discusses the technology behind Crop.photo's ability to enhance online stores, from fashion to furniture, creating eye-catching digital storefronts. Tune in for an exciting glimpse into the future of Gen AI-driven bulk image editing that's reshaping the online shopping experience.

AWS Hosts: Vaishnavi Ganesan & Malini Chatterjee

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Get Started for Free: https://app.crop.photo/user/signup

In the rapidly evolving world of cloud computing, financial operations (FinOps) has emerged as a critical discipline for organizations looking to optimize their cloud spending and mitigate financial risks. In this episode of the AWS re:Think podcast, we dive deep into the latest advancements in the FinOps space, exploring how organizations can navigate the financial complexities of cloud deployment and management. Joining us is Aran Khanna, the Co-founder and CEO of Archera.ai, a leading provider of FinOps solutions. Aran shares his insights on the importance of FinOps, the latest tools and strategies, and how Archera's innovative offerings, including their "Insured Commitments" program, can help customers mitigate the financial risks associated with the cloud. Whether you're in the midst of a cloud migration or looking to optimize your existing cloud infrastructure, this episode is a must-listen for anyone looking to rethink their cloud strategy and maximize the financial benefits of the cloud. 

Check out Archera.ai at https://archera.ai/ to learn more.

AWS Hosts: Nolan Chen & Anu Jayanthi

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Companies now have a wide array of choices when it comes to Large Language Models. In this episode we talk to Ivan Lee, CEO of datasaur.ai about choosing the right LLM for your AI projects. We discuss open source vs proprietary models and the metrics for evaluating these models. We also cover Small Language Models as well and when to use more specialized vs general purpose models.

Datasaur:

https://datasaur.ai/

LLM Labs from Datasaur:

https://datasaur.ai/llm/home

AWS Hosts: Nolan Chen & Malini Chatterjee

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Companies need to provide real time insights to both customers and internal users. These insights power use cases such as personalization and fraud detection. StarTree Cloud is a real-time analytics platform built on Apache Pinot for building such applications that depend on real time insights. In this episode we meet with Chinmay Soman, Head of Product at Startree.ai to discuss the different dimensions of real-time analytics and how Apache Pinot and StarTree Cloud offer a robust platform for providing such insights to applications.

AWS Hosts: Nolan Chen & Malini Chatterjee

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Resources:

StarTree:

https://startree.ai

StarTree community Slack:

https://communityinviter.com/apps/startreedata/startree-community

Apache Pinot Slack: 

https://communityinviter.com/apps/apache-pinot/apache-pinot

Serverless / Free forever workspace:

https://stree.ai/free

Today we join Maria Khalusova, Staff Developer Advocate with Unstructured.IO, to discuss how companies can unlock their Unstructured Data to deliver better results from their Large Language Models. We talk about how Unstructured Data can enhance the performance of RAG applications, RAG vs Fine Tuning, data Chunking, Multi-Modal models and more. 

AWS Hosts: Nolan Chen & Malini Chatterjee

Unstructured Enterprise Platform beta signup: 

https://unstructured.io/platform

Embedding models MTEB Leaderboard: 

https://huggingface.co/spaces/mteb/leaderboard

2019 Deloitte report (source of the statistics that only 18% of organizations were using unstructured data):

https://www2.deloitte.com/us/en/insights/topics/analytics/insight-driven-organization.html

80% of data is unstructured, source: https://mitsloan.mit.edu/ideas-made-to-matter/tapping-power-unstructured-data

Papers showing RAG outperforming fine-tuning: 

https://arxiv.org/abs/2312.05934

https://arxiv.org/abs/2401.08406

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