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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 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/Timestamps:[00:00] Wei's preferred coffee[00:26] Takeaways[00:50] Please like, share, leave a review, and subscribe to our MLOps channels! [01:06] PII Anonymization Techniques[09:49] Graph RAG Differentiation Ideas[19:55] Ontologies vs Embeddings in AI[30:05] Graph Exploration and Insight [39:25] Iceberg Data Metaphor[41:19] Contextual Data Visualization[42:44] Granularity vs Domain Shifting[49:51] Visualization Access Control[53:37] Graph RAG Use Cases[59:16] IoT and Graphs[1:01:15] Data Visualization[1:12:14] Wrap up

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/Timestamps:[00:00] Vikram's preferred coffee[00:09] Takeaways[00:42] Please like, share, leave a review, and subscribe to our MLOps channels! You can give us up to 5 stars on Spotify and leave your reviews![01:53] Product User Categories[02:47] AI Data Engineer Role[05:40] AI Coding Limits Enterprise[09:22] Creating Feedback Loops[14:23] Breaking Down Big Tasks[19:39] Marketing Data Agent Scope[28:03] Clear Success Metrics[32:20] Creating Agent Glossary[36:43] AI Prompt Toolkits[38:54] Pricing Strategy Discussion[43:20] Compute Abstraction and Pipelines[45:23] Agent Surprises and Logs[47:12] Wrap up

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.

Luca Fiaschi, a Partner at PyMC Labs with extensive experience in AI and data science, shares insights on revolutionizing product ideation through synthetic consumer testing. He discusses how his innovative multi-agent system accelerates concept discovery, compressing traditional development timelines. The conversation highlights the integration of AI in demand forecasting and marketing analytics, emphasizing the importance of human oversight. Fiaschi also explores leveraging behavioral insights for user experience enhancement and the evolving role of consulting in data-driven decision-making.

Josh Xi, a Data Scientist at Lyft with a PhD in Operations Research, shares insights on real-time forecasting methods for marketplace dynamics. He discusses the effectiveness of time series models over deep neural networks, emphasizing their adaptability and efficiency. Listeners will learn about integrating external data like weather and events into demand forecasts. Xi also delves into the complexities of geographical forecasting and the role of human insights in enhancing predictive accuracy, making it a must-listen for data enthusiasts!

Tanmay Chopra, CEO of Emissary and a machine learning expert, dives into the future of finetuning in AI. He challenges the notion that traditional finetuning is obsolete, revealing new methods that truly optimize models. The conversation covers the role of AI in both expert and novice settings, the shift back to simpler solutions, and how no-code tools are revolutionizing development. Tanmay also highlights the critical importance of localization in advertising, emphasizing small adjustments that yield big cultural impacts.

David Cox, VP of Data Science at RethinkFirst, dives into the maturation of AI across industries. He discusses the shift from merely adopting shiny new technologies to analyzing ROI and focusing on market differentiation. The conversation covers how AI can enhance clinical decision-making and address behavioral change by leveraging data. Cox emphasizes the importance of user agency in AI's potential to empower healthier choices, alongside the need to navigate distractions that technology introduces into daily habits.