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Rahul Parundekar, Founder of AI Hero, Inc., and Sam Partee, CTO of Arcade AI, dive into the complexities of AI agents and tools. They tackle the significance of digital permissions and the challenges of agent-to-agent interactions. The duo unveils the intricacies of authentication processes, emphasizing OAuth's role in security. They also discuss the evolution of agent-based programming and AI tools, highlighting the need for improved evaluation methods. With humor, they address the frustrations of automated email responses while celebrating AI's potential to transform workflows.

Kison Patel, Founder and CEO of DealRoom and M&A Science, shares his insights on the intersection of AI and M&A. He discusses how AI capabilities are transforming deal-making and what makes AI companies attractive targets. Kison emphasizes the importance of a buyer-led M&A strategy and shares strategies for effective team building and communication. He also explores the challenges of pricing AI products and the cautious valuation approaches in the rapidly evolving market. Tune in for a thought-provoking look at the future of deal-making!

Fausto Albers, an AI Engineer and community lead at AI Builders Club Amsterdam, dives into the interplay between AI, marketing, and human decision-making. He discusses the exciting potential of generative AI in transforming creative workflows and A/B testing while cautioning against the risks of diminishing critical thinking. The conversation also touches on the importance of human connection in an automated world and how technological integration must remain balanced with personal interactions in various industries, especially hospitality.

Maria Vechtomova, an MLOps Tech Lead and co-founder of Marvelous MLOps, shares her insights on the complexities of MLOps and the advantages of using Databricks. She discusses the challenges data scientists face transitioning from notebooks to production-ready models and stresses the importance of model packaging. The conversation also touches on emerging terms like 'LLM Ops,' new features in MLflow, and the practical uses of Databricks for model serving. Plus, she mentions an upcoming hands-on course and a book on Databricks, aimed at enhancing the learning experience.

Alon Bochman, CEO of RagMetrics and AI veteran, dives into the complexities of making AI reliable. He emphasizes empirical evaluation over influencer advice, advocating for collaboration between technical and domain experts. Alon discusses the importance of tailoring AI solutions and involving subject matter experts in development. The conversation also covers fine-tuning language models through expert feedback and the challenges of AI in finance, highlighting the need for effective knowledge-sharing to enhance accuracy in decision-making.

In this engaging discussion, Devansh Devansh, an open-source AI researcher and Head of AI at a stealth startup, shares insights on grounded AI research and the biases present in data. He emphasizes the balance between deterministic systems and autonomous agents, urging a rethink of data infrastructures. The conversation delves into the potential of synthetic data for reducing biases and enhancing fairness, encouraging listeners to consider ethical implications. Devansh also highlights the critical role of behavioral modeling in improving user experiences and insights.

GraphBI: Expanding Analytics to All Data Through the Combination of GenAI, Graph, & Visual Analytics // MLOps Podcast #310 with Paco Nathan, Principal DevRel Engineer at Senzing & Weidong Yang, CEO of Kineviz.Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // AbstractExisting BI and big data solutions depend largely on structured data, which makes up only about 20% of all available information, leaving the vast majority untapped. In this talk, we introduce GraphBI, which aims to address this challenge by combining GenAI, graph technology, and visual analytics to unlock the full potential of enterprise data.Recent technologies like RAG (Retrieval-Augmented Generation) and GraphRAG leverage GenAI for tasks such as summarization and Q&A, but they often function as black boxes, making verification challenging. In contrast, GraphBI uses GenAI for data pre-processing—converting unstructured data into a graph-based format—enabling a transparent, step-by-step analytics process that ensures reliability.We will walk through the GraphBI workflow, exploring best practices and challenges in each step of the process: managing both structured and unstructured data, data pre-processing with GenAI, iterative analytics using a BI-focused graph grammar, and final insight presentation. This approach uniquely surfaces business insights by effectively incorporating all types of data.// BioPaco NathanPaco Nathan is a "player/coach" who excels in data science, machine learning, and natural language, with 40 years of industry experience. He leads DevRel for the Entity Resolved Knowledge Graph practice area at Senzing.com and advises Argilla.io, Kurve.ai, KungFu.ai, and DataSpartan.co.uk, and is lead committer for the pytextrank​ and kglab​ open source projects. Formerly: Director of Learning Group at O'Reilly Media; and Director of Community Evangelism at Databricks.Weidong YangWeidong Yang, Ph.D., is the founder and CEO of Kineviz, a San Francisco-based company that develops interactive visual analytics based solutions to address complex big data problems. His expertise spans Physics, Computer Science and Performing Art, with significant contributions to the semiconductor industry and quantum dot research at UC, Berkeley and Silicon Valley. Yang also leads Kinetech Arts, a 501(c) non-profit blending dance, science, and technology. An eloquent public speaker and performer, he holds 11 US patents, including the groundbreaking Diffraction-based Overlay technology, vital for sub-10-nm semiconductor production.// Related LinksWebsite: https://www.kineviz.com/Blog: https://medium.com/kinevizWebsite: https://derwen.ai/pacohttps://huggingface.co/pacoidhttps://github.com/ceterihttps://neo4j.com/developer-blog/entity-resolved-knowledge-graphs/~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Weidong on LinkedIn: /yangweidong/Connect with Paco on LinkedIn: /ceteri/

AI Data Engineers - Data Engineering after AI // MLOps Podcast #309 with Vikram Chennai, Founder/CEO of Ardent AI.Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // AbstractA discussion of Agentic approaches to Data Engineering. Exploring the benefits and pitfalls of AI solutions and how to design product-grade AI agents, especially in data.// BioSecond Time Founder. 5 years building Deep learning models. Currently, AI Data Engineers// Related LinksWebsite: tryardent.com~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Vikram on LinkedIn: /vikram-chennai/

Oleksandr Stasyk, Engineering Manager of ML Platform at Synthesia, dives into the evolving landscape of MLOps. He highlights the need for solid software engineering practices amidst the rush to monetize AI. The conversation touches on the significance of data engineering and the pitfalls of 'vibe coding,' emphasizing structured practices. Oleksandr also discusses the importance of team collaboration for breaking down silos in MLOps and the critical role of early testing to mitigate risks in production. It’s a standout discussion on balancing innovation with practicality!

Duncan Curtis, SVP of Product and Technology at Sama, shares insights from his extensive background in autonomous vehicles and AI. He delves into the crucial role of data annotation in enhancing ML model accuracy for AVs. The discussion addresses the challenges of integrating human input into AI systems and how this can improve safety and efficiency. Curtis also highlights the importance of modernizing data strategies and overcoming bottlenecks in data accessibility, ensuring AI technology can deliver reliable and safe autonomous driving experiences.