talk-data.com

At Qdrant Conference, builders, researchers, and industry practitioners shared how vector search, retrieval infrastructure, and LLM-driven workflows are evolving across developer tooling, AI platforms, analytics teams, and modern search research.

Andrey Vasnetsov (Qdrant) explained how Qdrant was born from the need to combine database-style querying with vector similarity search—something he first built during the COVID lockdowns. He highlighted how vector search has shifted from an ML specialty to a standard developer tool and why hosting an in-person conference matters for gathering honest, real-time feedback from the growing community.

Slava Dubrov (HubSpot) described how his team uses Qdrant to power AI Signals, a platform for embeddings, similarity search, and contextual recommendations that support HubSpot’s AI agents. He shared practical use cases like look-alike company search, reflected on evaluating agentic frameworks, and offered career advice for engineers moving toward technical leadership.

Marina Ariamnova (SumUp) presented her internally built LLM analytics assistant that turns natural-language questions into SQL, executes queries, and returns clean summaries—cutting request times from days to minutes. She discussed balancing analytics and engineering work, learning through real projects, and how LLM tools help analysts scale routine workflows without replacing human expertise.

Evgeniya (Jenny) Sukhodolskaya (Qdrant) discussed the multi-disciplinary nature of DevRel and her focus on retrieval research. She shared her work on sparse neural retrieval, relevance feedback, and hybrid search models that blend lexical precision with semantic understanding—contributing methods like Mini-COIL and shaping Qdrant’s search quality roadmap through end-to-end experimentation and community education.

Speakers

Andrey Vasnetsov Co-founder & CTO of Qdrant, leading the engineering and platform vision behind a developer-focused vector database and vector-native infrastructure. Connect: https://www.linkedin.com/in/andrey-vasnetsov-75268897/

Slava Dubrov Technical Lead at HubSpot working on AI Signals—embedding models, similarity search, and context systems for AI agents. Connect: https://www.linkedin.com/in/slavadubrov/

Marina Ariamnova Data Lead at SumUp, managing analytics and financial data workflows while prototyping LLM tools that automate routine analysis. Connect: https://www.linkedin.com/in/marina-ariamnova/

Evgeniya (Jenny) Sukhodolskaya Developer Relations Engineer at Qdrant specializing in retrieval research, sparse neural methods, and educational ML content. Connect: https://www.linkedin.com/in/evgeniya-sukhodolskaya/

Leadership in data-driven organizations requires a delicate balance of technical expertise and human understanding. As businesses navigate unprecedented uncertainty in global markets, geopolitics, and technological change, the role of data as a source of truth becomes increasingly vital. But how do you create a culture where data informs decisions at every level? What separates leaders who merely collect data from those who leverage it to drive bold, transformative action? For data professionals looking to advance their careers, the challenge extends beyond technical skills to understanding how data connects to broader business strategy and organizational purpose. Carolyn Dewar is the founder and global co-leader of McKinsey & Company’s CEO Practice, where she partners with CEOs, founders, boards, and senior executives to help them maximize their effectiveness and lead their organizations through critical moments, including hypergrowth, transformation, crises, and mergers. Drawing on her extensive research and experience, Carolyn works with leaders across all stages of the CEO journey to drive large-scale organizational change, set bold strategies, and shape company culture to align leadership teams, manage external stakeholders, and optimize executive time and operating models. She helps CEOs develop the mindsets and frameworks needed to succeed in their role, ensuring they deliver lasting impact and sustainable growth. A recognized thought leader, Carolyn is the co-author of CEO Excellence: The Six Mindsets That Distinguish the Best Leaders from the Rest (a New York Times bestseller) and A CEO for All Seasons: Mastering the Cycles of Leadership. She publishes the monthly Strategic CEO newsletter and has contributed over 30 articles to Harvard Business Review, The Conference Board, and McKinsey Quarterly. Carolyn is also a member of the McKinsey Global Institute Council, which advises on MGI’s research on global economic, business, and technology trends. With over 25 years of experience advising clients across industries, including financial services, technology, and consumer sectors, Carolyn is also a sought-after keynote speaker and panelist at global conferences. In the episode, Richie and Carolyn explore common mistakes for CEOs, the unique responsibilities of a CEO, the importance of data-driven decision-making, fostering a data-centric culture, aligning data and business strategies, and much more. Links Mentioned in the Show: CEO Excellence: The Six Mindsets That Distinguish the Best Leaders from the RestConnect with CarolynSkill Track: Artificial Intelligence (AI) LeadershipRelated Episode: From Panic to Profit, Via Data with Bill Canady, CEO at Arrowhead Engineered ProductsRewatch RADAR AI  New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills witha...

This session highlights Wipro's innovative approach to transforming financial operations using AI agents. By leveraging Wipro's deep industry expertise and Snowflake's data prowess, we have developed a scalable, secure, and intelligent system that revolutionizes the management of supplier-related queries around invoices and payments. Our solution has significantly increased processing efficiency, reduced latency and manual activities, and achieved high accuracy in responses. The implementation has led to substantial cost savings and improved response times, demonstrating the potential of AI-powered solutions in enhancing business operations.

Financial institutions are racing to harness the power of AI, but the path to implementation is filled with challenges. From feature engineering to model deployment, the technical complexities of AI adoption in finance require careful navigation of both technological and regulatory landscapes. How do you build AI systems that satisfy strict compliance requirements while still delivering business value? What skills should teams prioritize as AI tools become more accessible through natural language interfaces? With the pressure to reduce model development time from months to days, how can organizations maintain proper governance while still moving at the speed modern business demands? Vijay is a seasoned analytics, product, and technology executive. As EVP of Global Solutions & Analytics at Experian, he runs the department that creates Experian's Ascend financial AI platform. Promoted multiple times in eight years, Vijay now leads a team of more than 70 at Experian. He is one of the youngest execs at Experian, believing strongly in understanding and accepting risk. He has built and run data, engineering, and IT teams, and created market-leading products. In the episode, Richie and Vijay explore the impact of generative AI on the finance industry, the development of Experian's Ascend platform, the challenges of fraud prevention, education and compliance in AI deployment, and much more. Links Mentioned in the Show: ExperianExperian AscendConnect with VijayCourse: Implementing AI Solutions in BusinessRelated Episode: How Generative AI is Transforming Finance with Andrew Reiskind, CDO at MastercardRewatch RADAR AI 

New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business

Summary In this episode of the Data Engineering Podcast Effie Baram, a leader in foundational data engineering at Two Sigma, talks about the complexities and innovations in data engineering within the finance sector. She discusses the critical role of data at Two Sigma, balancing data quality with delivery speed, and the socio-technical challenges of building a foundational data platform that supports research and operational needs while maintaining regulatory compliance and data quality. Effie also shares insights into treating data as code, leveraging modern data warehouses, and the evolving role of data engineers in a rapidly changing technological landscape.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. This episode is brought to you by Coresignal, your go-to source for high-quality public web data to power best-in-class AI products. Instead of spending time collecting, cleaning, and enriching data in-house, use ready-made multi-source B2B data that can be smoothly integrated into your systems via APIs or as datasets. With over 3 billion data records from 15+ online sources, Coresignal delivers high-quality data on companies, employees, and jobs. It is powering decision-making for more than 700 companies across AI, investment, HR tech, sales tech, and market intelligence industries. A founding member of the Ethical Web Data Collection Initiative, Coresignal stands out not only for its data quality but also for its commitment to responsible data collection practices. Recognized as the top data provider by Datarade for two consecutive years, Coresignal is the go-to partner for those who need fresh, accurate, and ethically sourced B2B data at scale. Discover how Coresignal's data can enhance your AI platforms. Visit dataengineeringpodcast.com/coresignal to start your free 14-day trial. Your host is Tobias Macey and today I'm interviewing Effie Baram about data engineering in the finance sectorInterview IntroductionHow did you get involved in the area of data management?Can you start by outlining the role of data in the context of Two Sigma?What are some of the key characteristics of the types of data sources that you work with?Your role is leading "foundational data engineering" at Two Sigma. Can you unpack that title and how it shapes the ways that you think about what you build?How does the concept of "foundational data" influence the ways that the business thinks about the organizational patterns around data?Given the regulatory environment around finance, how does that impact the ways that you think about the "what" and "how" of the data that you deliver to data consumers?Being the foundational team for data use at Two Sigma, how have you approached the design and architecture of your technical systems?How do you think about the boundaries between your responsibilities and the rest of the organization?What are the design patterns that you have found most helpful in empowering data consumers to build on top of your work?What are some of the elements of sociotechnical friction that have been most challenging to address?What are the most interesting, innovative, or unexpected ways that you have seen the ideas around "foundational data" applied in your organization?What are the most interesting, unexpected, or challenging lessons that you have learned while working with financial data?When is a foundational data team the wrong approach?What do you have planned for the future of your platform design?Contact Info LinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links 2SigmaReliability EngineeringSLA == Service-Level AgreementAirflowParquet File FormatBigQuerySnowflakedbtGemini AssistMCP == Model Context ProtocoldtraceThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Today, I’m responding to a listener's question about what it takes to succeed as a data or AI product manager, especially if you’re coming from roles like design/BI/data visualization, data science/engineering, or traditional software product management. This reader correctly observed that most of my content “seems more targeted at senior leadership” — and had asked if I could address this more IC-oriented topic on the show. I’ll break down why technical chops alone aren’t enough, and how user-centered thinking, business impact, and outcome-focused mindsets are key to real success — and where each of these prior roles brings strengths and/or weaknesses. I’ll also get into the evolving nature of PM roles in the age of AI, and what I think the super-powered AI product manager will look like.

Highlights/ Skip to:

Who can transition into an AI and data product management role? What does it take? (5:29) Software product managers moving into  AI product management (10:05) Designers moving into data/AI product management (13:32) Moving into the AI PM role from the engineering side (21:47) Why the challenge of user adoption and trust is often the blocker to the business value (29:56) Designing change management into AI/data products as a skill (31:26) The challenge of value creation vs. delivery work — and how incentives are aligned for ICs  (35:17) Quantifying the financial value of data and AI product work(40:23)

Quotes from Today’s Episode

“Who can transition into this type of role, and what is this role? I’m combining these two things. AI product management often seems closely tied to software companies that are primarily leveraging AI, or trying to, and therefore, they tend to utilize this AI product management role. I’m seeing less of that in internal data teams, where you tend to see data product management more, which, for me, feels like an umbrella term that may include traditional analytics work, data platforms, and often AI and machine learning. I’m going to frame this more in the AI space, primarily because I think AI tends to capture the end-to-end product than data product management does more frequently.” — Brian (2:55)

“There are three disciplines I’m going to talk about moving into this role. Coming into AI and data PM from design and UX, coming into it from data engineering (or just broadly technical spaces), and then coming into it from software product management. I think software product management and moving into the AI product management - as long as you’re not someone that has two years of experience, and then 18 years of repeating the second year of experience over and over again - and you’ve had a robust product management background across some different types of products; you can show that the domain doesn’t necessarily stop you from producing value. I think you will have the easiest time moving into AI product management because you’ve shown that you can adapt across different industries.” - Brian (9:45)

“Let’s talk about designers next. I’m going to include data visualization, user experience research, user experience design, product design, all those types of broad design, category roles. Moving into data and/or AI product management, first of all, you don’t see too many—I don’t hear about too many designers wanting to move into DPM roles, because oftentimes I don’t think there’s a lot of heavy UI and UX all the time in that space. Or at least the teams that are doing that work feel that’s somebody else’s job because they’re not doing end-to-end product thinking the way I talk about it, so therefore, a lot of times they don’t see the application, the user experience, the human adoption, the change management, they’re just not looking at the world that way, even though I think they should be.” - Brian (13:32)

“Coming at this from the data and engineering side, this is the classic track for data product management. At least that is the way I tend to see it. I believe most companies prefer to develop this role in-house. My biggest concern is that you end up with job title changes, but not necessarily the benefits that are supposed to come with this. I do like learning by doing, but having a coach and someone senior who can coach your other PMs is important because there’s a lot of information that you won’t necessarily get in a class or a course. It’s going to come from experience doing the work.” - Brian (22:26)

“This value piece is the most important thing, and I want to focus on that. This is something I frequently discuss in my training seminar: how do we attach financial value to the work we’re doing? This is both art and science, but it’s a language that anyone in a product management role needs to be comfortable with. If you’re finding it very hard to figure out how your data product contributes financial value because it’s based on this waterfalling of “We own the model, and it’s deployed on a platform.” The platform then powers these other things, which in turn power an application. How do we determine the value of our tool? These things are challenging, and if it’s challenging for you, guess how hard it will be for stakeholders downstream if you haven’t had the practice and the skills required to understand how to estimate value, both before we build something as well as after?” - Brian (31:51)

“If you don’t want to spend your time getting to know how your business makes money or creates value, then [AI and data product management work] is not for you. It’s just not. I would stay doing what you’re doing already or find a different thing because a lot of your time is going to be spent “managing up” for half the time, and then managing the product stuff “down.” Then, sitting in this middle layer, trying to explain to the business what’s going to come out and what the impact is going to be, in language that they care about and understand. You can't be talking about models, model accuracy, data pipelines, and all that stuff. They’re not going to care about any of that. - Brian (34:08)

Summary In this episode of the Data Engineering Podcast Mai-Lan Tomsen Bukovec, Vice President of Technology at AWS, talks about the evolution of Amazon S3 and its profound impact on data architecture. From her work on compute systems to leading the development and operations of S3, Mylan shares insights on how S3 has become a foundational element in modern data systems, enabling scalable and cost-effective data lakes since its launch alongside Hadoop in 2006. She discusses the architectural patterns enabled by S3, the importance of metadata in data management, and how S3's evolution has been driven by customer needs, leading to innovations like strong consistency and S3 tables.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.This is a pharmaceutical Ad for Soda Data Quality. Do you suffer from chronic dashboard distrust? Are broken pipelines and silent schema changes wreaking havoc on your analytics? You may be experiencing symptoms of Undiagnosed Data Quality Syndrome — also known as UDQS. Ask your data team about Soda. With Soda Metrics Observability, you can track the health of your KPIs and metrics across the business — automatically detecting anomalies before your CEO does. It’s 70% more accurate than industry benchmarks, and the fastest in the category, analyzing 1.1 billion rows in just 64 seconds. And with Collaborative Data Contracts, engineers and business can finally agree on what “done” looks like — so you can stop fighting over column names, and start trusting your data again.Whether you’re a data engineer, analytics lead, or just someone who cries when a dashboard flatlines, Soda may be right for you. Side effects of implementing Soda may include: Increased trust in your metrics, reduced late-night Slack emergencies, spontaneous high-fives across departments, fewer meetings and less back-and-forth with business stakeholders, and in rare cases, a newfound love of data. Sign up today to get a chance to win a $1000+ custom mechanical keyboard. Visit dataengineeringpodcast.com/soda to sign up and follow Soda’s launch week. It starts June 9th.Your host is Tobias Macey and today I'm interviewing Mai-Lan Tomsen Bukovec about the evolutions of S3 and how it has transformed data architectureInterview IntroductionHow did you get involved in the area of data management?Most everyone listening knows what S3 is, but can you start by giving a quick summary of what roles it plays in the data ecosystem?What are the major generational epochs in S3, with a particular focus on analytical/ML data systems?The first major driver of analytical usage for S3 was the Hadoop ecosystem. What are the other elements of the data ecosystem that helped shape the product direction of S3?Data storage and retrieval have been core primitives in computing since its inception. What are the characteristics of S3 and all of its copycats that led to such a difference in architectural patterns vs. other shared data technologies? (e.g. NFS, Gluster, Ceph, Samba, etc.)How does the unified pool of storage that is exemplified by S3 help to blur the boundaries between application data, analytical data, and ML/AI data?What are some of the default patterns for storage and retrieval across those three buckets that can lead to anti-patterns which add friction when trying to unify those use cases?The age of AI is leading to a massive potential for unlocking unstructured data, for which S3 has been a massive dumping ground over the years. How is that changing the ways that your customers think about the value of the assets that they have been hoarding for so long?What new architectural patterns is that generating?What are the most interesting, innovative, or unexpected ways that you have seen S3 used for analytical/ML/Ai applications?What are the most interesting, unexpected, or challenging lessons that you have learned while working on S3?When is S3 the wrong choice?What do you have planned for the future of S3?Contact Info LinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links AWS S3KinesisKafkaSQSEMRDrupalWordpressNetflix Blog on S3 as a Source of TruthHadoopMapReduceNasa JPLFINRA == Financial Industry Regulatory AuthorityS3 Object VersioningS3 Cross RegionS3 TablesIcebergParquetAWS KMSIceberg RESTDuckDBNFS == Network File SystemSambaGlusterFSCephMinIOS3 MetadataPhotoshop Generative FillAdobe FireflyTurbotax AI AssistantAWS Access AnalyzerData ProductsS3 Access PointAWS Nova ModelsLexisNexis ProtegeS3 Intelligent TieringS3 Principal Engineering TenetsThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Join us to explore Lesley Li’s entrepreneurial journey, from banking to pioneering sustainable FinTech, @ the SIBB Incubator, April 11, 2025

Lesley Li is the CEO and co-founder of U Impact, a Berlin-based FinTech. Prior to founding U Impact, she spent over a decade at JP Morgan, Barclays, and Mizuho in London, gaining deep expertise in financial markets. She holds two Master’s degrees in Finance and Industrial Management from the University of Cambridge. Passionate about sustainability and equality, she regularly speaks at major global conferences, addressing topics on sustainable finance, financial inclusion, and the future of investing.

At U Impact, we leverage Generative AI and behavioral science to activate and engage the next generation of investors - including women, young people, and sustainability-focused investors. Our unique approach transforms the investment experience, making it accessible, impactful, and aligned with investors' personal values and aspirations. As an award-winning startup, we're proud winners of the Morningstar Green FinTech competition, recognized for our innovation in mobilizing capital towards sustainable finance and financial inclusion.

Ageda:

• 15:30 Arrival & Welcome
• 15:45 Event Begins
• 17:30 Networking

About the moderator: Urte Zahn hosts our SIBB event series Ask Female Founders since two years. She loves asking guests questions and at the same time engaging the audience. Beside this Urte coaches female entrepreneurs of tech startups and founded serveral times herself in different industries.

Register to reserve your spot.

When and Where

April 3\, 2025 \| 5:00 – 8:00 PM 393 Broadway 2nd Floor, New York, NY 10013

Orchestrating AI Pipelines: GenAI and LLMOps in Action

The increasing adoption of Generative AI (GenAI) is transforming decision-making across industries, making robust Large Language Model Operations (LLMOps) and governance strategies critical. This presentation examines technical approaches for leveraging pre-trained transformer models to extract actionable insights and explores the implementation of secure and compliant LLMOps pipelines. It will address the challenges and solutions for establishing safe GenAI practices, particularly within regulated sectors like pharmaceuticals, emphasizing the crucial role of governance and risk mitigation in ensuring responsible data-driven decision-making.

About the Speakers

Shihan He is a Machine Learning Engineer at Novo Nordisk, focus on developing and implementing advanced AI solutions to drive valuable decision-making throughout the healthcare value chain. She is passionate about applying AI to improve patient outcomes, particularly for those with chronic diseases. Shihan is particularly interested in exploring the potential of GenAI and LLOps to build robust and scalable AI systems.

Anubhav Srivastava is the Associate Director of the AI Center of Excellence at Novo Nordisk, leading the development and deployment of AI-powered solutions across the organization. He possesses extensive experience in building and managing high-performing AI teams and is passionate about fostering a culture of innovation and collaboration. Anubhav is committed to driving the adoption of cutting-edge AI technologies, such as GenAI and LLOps, to address critical business challenges and improve patient outcomes.

Trading on Vision: The Role of VLMs in Market Insights

Financial markets move at the speed of information, and traders are increasingly looking beyond traditional text-based data for an edge. Vision-Language Models (VLMs) offer a revolutionary approach by analyzing both visual and textual financial data – from satellite imagery and corporate filings to stock charts and social media trends. This talk explores how VLMs can extract alternative trading signals, interpret financial reports beyond text, and analyze sentiment from news images, all in real time. We’ll also discuss the challenges of accuracy, bias, and regulation when applying VLMs to financial decision-making. By integrating multimodal AI into trading strategies, investors can see the markets like never before.

About the Speaker

Daniel Gural is a seasoned Machine Learning Evangelist with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data. Currently serving as a valuable member of Voxel51, he takes a leading role in efforts to bridge the gap between practitioners and the necessary tools, enabling them to achieve exceptional outcomes. Daniel’s extensive experience in teaching and developing within the ML field has fueled his commitment to democratizing high-quality AI workflows for a wider audience.

Learning About Data Through Models

As data-centric AI continues to grow, more time is spent curating data for better training outcomes. Over the last few years, the script has been flipped. Nowadays, models are used to inform data curation, creating a feedback loop of incremental improvements to data and models side-by-side. We’ll review some literature from the last few years, and see how we can use FiftyOne to quickly start model training on any FiftyOne dataset.

About the Speaker

Jacob Sela has been developing deep learning since StyleGAN first captured his imagination in 2018. Having worked through the model life cycle from idea to production dozens of times, Jacob is now using this expertise at Voxel51 to push forward data centric AI for all developers.

Trust and Security in GenAI

With the growing ubiquity of GenAI use-cases and deployment across industries, new vulnerabilities and risks are emerging that are not addressed by traditional cyber security or software development guardrails. This session will review examples of new Large-Language-Model (LLM) and Computer Vision use cases in a number of industries (financial services, telco, automotive, and more), the new vulnerabilities they create, and ways to mitigate such new threats and risks.

About the Speaker

Ken Zamkow is an experienced entrepreneur and tech executive, and leads the US activities of DeepKeep.ai, an AI-native trust and security platform for GenAI. Ken is passionate about bringing new technology products to market. He previously led business development and go-to-market for 3 acquired startups: Run:AI — acquired by Nvidia; LiveU — acquired by Francisco Partners; GoParrot — acquired by Square/Block; and has also launched new AI and developer tools as part of Intel’s Emerging Growth and Incubation group.

Register to reserve your spot.

When and Where

April 3\, 2025 \| 5:00 – 8:00 PM 393 Broadway 2nd Floor, New York, NY 10013

Orchestrating AI Pipelines: GenAI and LLMOps in Action

The increasing adoption of Generative AI (GenAI) is transforming decision-making across industries, making robust Large Language Model Operations (LLMOps) and governance strategies critical. This presentation examines technical approaches for leveraging pre-trained transformer models to extract actionable insights and explores the implementation of secure and compliant LLMOps pipelines. It will address the challenges and solutions for establishing safe GenAI practices, particularly within regulated sectors like pharmaceuticals, emphasizing the crucial role of governance and risk mitigation in ensuring responsible data-driven decision-making.

About the Speakers

Shihan He is a Machine Learning Engineer at Novo Nordisk, focus on developing and implementing advanced AI solutions to drive valuable decision-making throughout the healthcare value chain. She is passionate about applying AI to improve patient outcomes, particularly for those with chronic diseases. Shihan is particularly interested in exploring the potential of GenAI and LLOps to build robust and scalable AI systems.

Anubhav Srivastava is the Associate Director of the AI Center of Excellence at Novo Nordisk, leading the development and deployment of AI-powered solutions across the organization. He possesses extensive experience in building and managing high-performing AI teams and is passionate about fostering a culture of innovation and collaboration. Anubhav is committed to driving the adoption of cutting-edge AI technologies, such as GenAI and LLOps, to address critical business challenges and improve patient outcomes.

Trading on Vision: The Role of VLMs in Market Insights

Financial markets move at the speed of information, and traders are increasingly looking beyond traditional text-based data for an edge. Vision-Language Models (VLMs) offer a revolutionary approach by analyzing both visual and textual financial data – from satellite imagery and corporate filings to stock charts and social media trends. This talk explores how VLMs can extract alternative trading signals, interpret financial reports beyond text, and analyze sentiment from news images, all in real time. We’ll also discuss the challenges of accuracy, bias, and regulation when applying VLMs to financial decision-making. By integrating multimodal AI into trading strategies, investors can see the markets like never before.

About the Speaker

Daniel Gural is a seasoned Machine Learning Evangelist with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data. Currently serving as a valuable member of Voxel51, he takes a leading role in efforts to bridge the gap between practitioners and the necessary tools, enabling them to achieve exceptional outcomes. Daniel’s extensive experience in teaching and developing within the ML field has fueled his commitment to democratizing high-quality AI workflows for a wider audience.

Learning About Data Through Models

As data-centric AI continues to grow, more time is spent curating data for better training outcomes. Over the last few years, the script has been flipped. Nowadays, models are used to inform data curation, creating a feedback loop of incremental improvements to data and models side-by-side. We’ll review some literature from the last few years, and see how we can use FiftyOne to quickly start model training on any FiftyOne dataset.

About the Speaker

Jacob Sela has been developing deep learning since StyleGAN first captured his imagination in 2018. Having worked through the model life cycle from idea to production dozens of times, Jacob is now using this expertise at Voxel51 to push forward data centric AI for all developers.

Trust and Security in GenAI

With the growing ubiquity of GenAI use-cases and deployment across industries, new vulnerabilities and risks are emerging that are not addressed by traditional cyber security or software development guardrails. This session will review examples of new Large-Language-Model (LLM) and Computer Vision use cases in a number of industries (financial services, telco, automotive, and more), the new vulnerabilities they create, and ways to mitigate such new threats and risks.

About the Speaker

Ken Zamkow is an experienced entrepreneur and tech executive, and leads the US activities of DeepKeep.ai, an AI-native trust and security platform for GenAI. Ken is passionate about bringing new technology products to market. He previously led business development and go-to-market for 3 acquired startups: Run:AI — acquired by Nvidia; LiveU — acquired by Francisco Partners; GoParrot — acquired by Square/Block; and has also launched new AI and developer tools as part of Intel’s Emerging Growth and Incubation group.

Register to reserve your spot.

When and Where

April 3\, 2025 \| 5:00 – 8:00 PM 393 Broadway 2nd Floor, New York, NY 10013

Orchestrating AI Pipelines: GenAI and LLMOps in Action

The increasing adoption of Generative AI (GenAI) is transforming decision-making across industries, making robust Large Language Model Operations (LLMOps) and governance strategies critical. This presentation examines technical approaches for leveraging pre-trained transformer models to extract actionable insights and explores the implementation of secure and compliant LLMOps pipelines. It will address the challenges and solutions for establishing safe GenAI practices, particularly within regulated sectors like pharmaceuticals, emphasizing the crucial role of governance and risk mitigation in ensuring responsible data-driven decision-making.

About the Speakers

Shihan He is a Machine Learning Engineer at Novo Nordisk, focus on developing and implementing advanced AI solutions to drive valuable decision-making throughout the healthcare value chain. She is passionate about applying AI to improve patient outcomes, particularly for those with chronic diseases. Shihan is particularly interested in exploring the potential of GenAI and LLOps to build robust and scalable AI systems.

Anubhav Srivastava is the Associate Director of the AI Center of Excellence at Novo Nordisk, leading the development and deployment of AI-powered solutions across the organization. He possesses extensive experience in building and managing high-performing AI teams and is passionate about fostering a culture of innovation and collaboration. Anubhav is committed to driving the adoption of cutting-edge AI technologies, such as GenAI and LLOps, to address critical business challenges and improve patient outcomes.

Trading on Vision: The Role of VLMs in Market Insights

Financial markets move at the speed of information, and traders are increasingly looking beyond traditional text-based data for an edge. Vision-Language Models (VLMs) offer a revolutionary approach by analyzing both visual and textual financial data – from satellite imagery and corporate filings to stock charts and social media trends. This talk explores how VLMs can extract alternative trading signals, interpret financial reports beyond text, and analyze sentiment from news images, all in real time. We’ll also discuss the challenges of accuracy, bias, and regulation when applying VLMs to financial decision-making. By integrating multimodal AI into trading strategies, investors can see the markets like never before.

About the Speaker

Daniel Gural is a seasoned Machine Learning Evangelist with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data. Currently serving as a valuable member of Voxel51, he takes a leading role in efforts to bridge the gap between practitioners and the necessary tools, enabling them to achieve exceptional outcomes. Daniel’s extensive experience in teaching and developing within the ML field has fueled his commitment to democratizing high-quality AI workflows for a wider audience.

Learning About Data Through Models

As data-centric AI continues to grow, more time is spent curating data for better training outcomes. Over the last few years, the script has been flipped. Nowadays, models are used to inform data curation, creating a feedback loop of incremental improvements to data and models side-by-side. We’ll review some literature from the last few years, and see how we can use FiftyOne to quickly start model training on any FiftyOne dataset.

About the Speaker

Jacob Sela has been developing deep learning since StyleGAN first captured his imagination in 2018. Having worked through the model life cycle from idea to production dozens of times, Jacob is now using this expertise at Voxel51 to push forward data centric AI for all developers.

Trust and Security in GenAI

With the growing ubiquity of GenAI use-cases and deployment across industries, new vulnerabilities and risks are emerging that are not addressed by traditional cyber security or software development guardrails. This session will review examples of new Large-Language-Model (LLM) and Computer Vision use cases in a number of industries (financial services, telco, automotive, and more), the new vulnerabilities they create, and ways to mitigate such new threats and risks.

About the Speaker

Ken Zamkow is an experienced entrepreneur and tech executive, and leads the US activities of DeepKeep.ai, an AI-native trust and security platform for GenAI. Ken is passionate about bringing new technology products to market. He previously led business development and go-to-market for 3 acquired startups: Run:AI — acquired by Nvidia; LiveU — acquired by Francisco Partners; GoParrot — acquired by Square/Block; and has also launched new AI and developer tools as part of Intel’s Emerging Growth and Incubation group.

In this episode of Hub & Spoken, host Jason Foster welcomes Sam White, the multi-award-winning Founder of Freedom Services Group and Global Founder of Stella Insurance Australia.  Sam shares her journey of building Stella Insurance, the challenges and opportunities of creating a digital-first insurance company, the importance of customer experience, and how Stella Insurance is reimagining financial services from a female perspective. Sam also discusses the impact of regulatory changes, the role of AI in the insurance industry, and the significance of diversity in business. This is a real gem of an episode, especially for entrepreneurs and business leaders interested in digital transformation, insurance innovations, and diversity in leadership.  Follow Sam: linkedin.com/in/samwhiteentrepreneur/ Follow Jason: linkedin.com/in/jasonbfoster/ *****    Cynozure is a leading data, analytics and AI company that helps organisations to reach their data potential. It works with clients on data and AI strategy, data management, data architecture and engineering, analytics and AI, data culture and literacy, and data leadership. The company was named one of The Sunday Times' fastest-growing private companies in both 2022 and 2023 and recognised as The Best Place to Work in Data by DataIQ in 2023 and 2024. Cynozure is a certified B Corporation. 

When: Thursday 20th February 2025 Time: arrive for 5:45pm with talks starting from 6pm start prompt.

Location: BJSS, 90 Victoria St, Redcliffe, BS1 6DP

Complimentary drinks & pizza provided by our hosts BJSS

Social drinks afterwards will be at Ye Shakespeare (50m away) for those interested.

This #MSFABRICBRISTOL will feature Chris Barber and Rishi Sapra

Session 1 - P&L Models - Chris Barber Profitability is a key metric; any profits can be distributed back to shareholders (owners) either directly or indirectly. A profit and loss (P&L) statement answers high level questions - such as “what was net profit last year?” - and summarises key revenue (i.e., product revenue) and expense (i.e., Research & Development) items. Unlike static reports, a P&L semantic model contains the detail; this allows end-users to ask questions such as “what was R&D spending broken down by a particular research project, fiscal period, or legal entity?” A semantic model also allows end-users to consume information using Copilot prompts, Power BI reports, or Pivot Tables and formulas in Excel. In this session, we will go through:

• Why you should build an income statement semantic model
• How to use an accelerator to speed up development.
• The key questions you need to ask stakeholders.
• The challenges in building a P&L income statement

About Chris Chris Barber is a chartered accountant (ACMA, CGMA), 4 times Microsoft MVP, author of Income Statement Semantic Models, runs StarSchema.co.uk, and has popular YouTube videos (over 100k views) and courses (over 1k participants) on the topic of Profit and Loss (P&L) using Power BI.

Session 2 - Financial Reporting in PBI - Rishi Sapra This session will explore how to transform a traditional Excel-based credit rating model for countries into a modern, interactive, and scalable solution using Power BI and Microsoft Fabric. Participants will discover innovative techniques to enhance financial modelling and storytelling by leveraging advanced analytics and integration capabilities.

Hear from Power Platform in Finance specialist and Microsoft MVP, Rishi Sapra, as we delve into the following areas:

• Replicating Complex Rating Logic: Translating intricate credit rating formulas and methodologies from Excel into DAX to ensure consistency and accuracy.
• Interactive Insights: Using slicers, bookmarks, and contextual visualizations in Power BI to enable dynamic comparisons and highlight key drivers of credit ratings.
• Streamlined Reporting: Designing intuitive and user-friendly reports that empower stakeholders to explore insights interactively.
• Scalability with Microsoft Fabric: Harnessing the Medallion Lakehouse architecture to enable seamless data integration, metadata management, and collaboration at an enterprise level.

This session is particularly beneficial for finance professionals and analysts who are seeking to modernize their financial or operational models. Attendees will leave with practical strategies to maintain model accuracy, enhance interactivity, and scale their solutions to meet the evolving demands of modern analytics.

About Rishi Rishi has a unique blend of technical and commercial skills - he is a 5x Microsoft Most Valuable Professional (MVP), a Chartered Accountant, a Microsoft Certified Trainer (MCT) and an Executive MBA (Hons) holder with a first class degree from the London School of Economics.

He currently works as a Data & AI Strategic Project lead at Avanade - a Joint Venture between Accenture and Microsoft which been awarded Microsoft Partner of the Year 18 times! In this role he focuses on Microsoft Fabric Go To Market (GTM) offerings including those around Security & Governance, Industry/Functional accelerators and Azure/BI migration.

Prior to joining Altius in 2017 (Which was acquired by Avanade in 2020), he spent 12 years in the Big 4 /Financial Services (Deloitte, HSBC, Barclays, KPMG) and has a strong background in Business modelling and process improvement.

He is actively involved in the Fabric Community in London/globally and has presented at Meetup groups and several large-scale conferences. His passion is in helping organisations achieve the holy grail of Self-Service BI and helping individuals learn the necessary skills to succeed. Under his community Learn Data Insights (www.learndatainsights.com), he runs Microsoft Fabric Training (www.msfabrictraining.com) and Power Platform Finance (www.powerplatformfinance.com) with e-learning courses and quizzes for Analytics and Finance professionals.

We all look forward to seeing you there!!

IAQF & Thalesians Seminar Series: Stress Testing Spillover Risk in Mutual Funds. A Seminar by Agostino Capponi.

6:00 PM Seminar Begins 7:30 PM Reception

Hybrid Event

Location: Fordham University McNally Amphitheater 140 West 62nd Street New York, NY 10023

Free Registration! For Virtual Attendees: Please email [email protected] for the link

Abstract: We develop a framework to quantify the vulnerability of mutual funds to fire-sale spillover losses. We account for the first-mover incentive that results from the mismatch between the liquidity offered to redeeming investors and the liquidity of assets held by the funds. In our framework, the negative feedback loop between investors’ redemptions and price impact from asset sales leads to an aggregate change in funds’ NAV, which is determined as a fixed point of a nonlinear mapping. We show that a higher concentration of first movers increases the aggregate vulnerability of the system, as measured by the ratio between endogenous losses due to fund redemptions and exogenous losses due to initial price shocks only. When calibrated to U.S. mutual funds, our model shows that, in stressed market scenarios, spillover losses are significantly amplified through a nonlinear response to initial shocks that results from the first-mover incentive. Higher spillover losses provide a stronger incentive to redeem early, further increasing fire-sale losses and the transmission of shocks through overlapping portfolio holdings.

Bio: Agostino Capponi is a Professor in the Department of Industrial Engineering and Operations Research at Columbia University, where he is also a member of the Data Science Institute and the founding director of the Columbia Center for Digital Finance and Technology. His current research interests are in financial technology, machine learning in finance, market microstructure, systemic and liquidity risk, climate finance, energy markets, and economic networks. Agostino's research has been funded by major agencies, including NSF, DARPA, DOE, IBM, GRI, INET, Ripple, Stellar, and the Ethereum foundation. His research has been recognized with the 2018 NSF CAREER award, a JP Morgan AI Research Faculty award, and the UBRI Innovator award. His research has also been covered by various media outlets, including Bloomberg, the Financial Times, Vox, and Politico. Agostino is a fellow of the crypto and blockchain economics research forum, and an academic fellow of Alibaba's Luohan academy. He serves as an editor of Management Science in the Finance Department, co-editor of Mathematics and Financial Economics, and financial engineering area editor of Operations Research. He has held editorial positions at several major journals in his field, such as the SIAM Journal on Financial Mathematics, Mathematical Finance, Finance and Stochastics, Operations Research Letters, Stochastic Systems, and Stochastic Models. Agostino is a past Chair of the SIAG/FME Activity Group and of the INFORMS Finance Section, and is currently a member of the Council of the Bachelier Finance Society. Agostino is co-editor of the book Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices, published in 2023 by the Cambridge University press.

The Data Product Management In Action podcast, brought to you by Soda and executive producer Scott Hirleman, is a platform for data product management practitioners to share insights and experiences. This episode of Data Product Management in Action, host Michael Toland sits down with Max Fritzhand, the creator of Bolta AI, to explore how he turned a simple frustration into a powerful platform for managing social media on Threads. Max discusses leveraging machine learning, navigating API challenges, and engaging users to create a feature-rich tool that offers analytics, content suggestions, and optimal posting times. A must-listen for innovators and product managers alike! About our host Michael Toland: Michael is a Product Management Coach and Consultant with Pathfinder Product, a Test Double Operation. Since 2016, Michael has worked on large-scale system modernizations and migration initiatives at Verizon. Outside his professional career, Michael serves as the Treasurer for the New Leaders Council, mentors with Venture for America, sings with the Columbus Symphony, and writes satire for his blog Dignified Product. He is excited to discuss data product management with the podcast audience. Connect with Michael on LinkedIn. About our guest Max Fritzhand: Max is a software engineer and entrepreneur based in Cincinnati, Ohio, with a passion for innovation and automation. With a background in developing user-centric tools, he’s built projects like Bolta to simplify social media management and empower creators. Known for his drive to achieve financial independence, he explores investment opportunities and strives to build impactful, scalable solutions. A creative thinker with ADHD, Max channels his energy into solving complex problems and building a fulfilling work-life balance. Connect with Max on LinkedIn. All views and opinions expressed are those of the individuals and do not necessarily reflect their employers or anyone else.  Join the conversation on LinkedIn.  Apply to be a guest or nominate someone that you know.  Do you love what you're listening to? Please rate and review the podcast, and share it with fellow practitioners you know. Your support helps us reach more listeners and continue providing valuable insights!   

Building and maintaining real-time fraud detection systems presents significant challenges, particularly when detecting and preventing fraud at scale while maintaining millisecond-level response times. North tackled these challenges head-on by developing an advanced in-house ML system that could better adapt to emerging fraud patterns.

In this Q&A with Ben Orkin, Vice President of Engineering - MLOps at North, we'll explore their journey from using a third-party solution to building their own fraud detection system powered by Tecton.

You'll learn about:

• The business drivers and technical requirements behind North's fraud detection model
• Key factors that influenced North's build vs. buy decisions for their ML infrastructure
• How North improved their ability to iterate and adapt to new fraud patterns
• The future of fraud detection and opportunities for innovation

Whether you're building fraud detection systems or working on real-time ML applications, this session offers valuable insights into architecting and scaling systems that demand both high accuracy and exceptional performance. Join us to learn how North built a fraud detection system that can adapt to emerging threats while maintaining the strict performance requirements of a high-volume financial services platform.

Register here

In the fast-paced world of mortgage lending, speed and accuracy are crucial. To support their underwriters, Vontive transformed written rules for loan eligibility from a Google Doc into SQL queries for evaluation in a Postgres database. However, while functional, this setup struggled to scale with business growth, resulting in slow, cumbersome processing times. Executing just a handful of loan eligibility rules could take up to 27 seconds–far too long for user-friendly interactions.

In this session, we’ll explore how Vontive reimagined its underwriting operations using Materialize. By offloading complex SQL queries from Postgres to Materialize, Vontive reduced eligibility check times from 27 seconds to under a second. This not only sped up decision-making but also removed limitations on the number of SQL-based underwriting rules, allowing underwriters to process more loans with greater accuracy and confidence. Additionally, this shift enabled the team to implement more automated checks throughout the underwriting process, catching errors earlier and further streamlining operations. Engineering needs were minimal, since DBT supports both cloud-based Postgres and Materialize.

Whether you're in financial services or any data-driven industry, this session offers valuable insights into leveraging fast-changing data for high-stakes decision-making with confidence.

Speakers: Steffen Hausmann Field Engineer Materialize

Wolf Rendall Director of Data Products Vontive

Read the blog to learn about the latest dbt Cloud features announced at Coalesce, designed to help organizations embrace analytics best practices at scale https://www.getdbt.com/blog/coalesce-2024-product-announcements

In today’s episode, I’m joined by John Felushko, a product manager at LabStats who impressed me after we recently had a 1x1 call together. John and his team have developed a successful product that helps universities track and optimize their software and hardware usage so schools make smart investments. However, John also shares how culture and value are very tied together—and why their product isn’t a fit for every school, and every country. John shares how important  customer relationships are , how his team designs great analytics user experiences, how they do user research, and what he learned making high-end winter sports products that’s relevant to leading a SAAS analytics product. Combined with John’s background in history and the political economy of finance, John paints some very colorful stories about what they’re getting right—and how they’ve course corrected over the years at LabStats. 

Highlights/ Skip to:

(0:46) What is the LabStats product  (2:59) Orienting analytics around customer value instead of IT/data (5:51) "Producer of Persistently Profitable Product Process" (11:22) How they make product adjustments based on previous failures (15:55) Why a lack of cultural understanding caused LabStats to fail internationally (18:43) Quantifying value beyond dollars and cents (25:23) How John is able to work so closely with his customers without barriers (30:24) Who makes up the LabStats product research team (35:04) ​​How strong customer relationships help inform the UX design process (38:29) Getting senior management to accept that you can't regularly and accurately predict when you’ll be feature-complete and ship (43:51) Where John learned his skills as a successful product manager (47:20) Where you can go to cultivate the non-technical skills to help you become a better SAAS analytics product leader (51:00) What advice would John Felushko have given himself 10 years ago? (56:19) Where you can find more from John Felushko

Quotes from Today’s Episode “The product process is [essentially] really nothing more than the scientific method applied to business. Every product is an experiment - it has a hypothesis about a problem it solves. At LabStats [we have a process] where we go out and clearly articulate the problem. We clearly identify who the customers are, and who are [people at other colleges] having that problem. Incrementally and as inexpensively as possible, [we] test our solutions against those specific customers. The success rate [of testing solutions by cross-referencing with other customers] has been extremely high.” - John Felushko (6:46) “One of the failures I see in Americans is that we don’t realize how much culture matters. Americans have this bias to believe that whatever is valuable in my culture is valuable in other cultures. Value is entirely culturally determined and subjective. Value isn’t a number on a spreadsheet. [LabStats positioned our producty] as something that helps you save money and be financially efficient. In French government culture, financial efficiency is not a top priority. Spending government money on things like education is seen as a positive good. The more money you can spend on it, the better.  So, the whole message of financial efficiency wasn’t going to work in that market.” - John Felushko (16:35) “What I’m really selling with data products is confidence. I’m selling assurance. I’m selling an emotion. Before I was a product manager, I spent about ten years in outdoor retail, selling backpacks and boots. What I learned from that is you’re always selling emotion, at every level. If you can articulate the ROI, the real value is that the buyer has confidence they bought the right thing.” - John Felushko (20:29) “[LabStats] has three massive, multi-million dollar horror stories in our past where we [spent] millions of dollars in development work for no results. No ROI. Horror stories are what shape people’s values more than anything else. Avoiding negative outcomes is what people avoid more than anything else. [It’s important to] tell those stories and perpetuate those [lessons] through the culture of your organization. These are the times we screwed up, and this is what we learned from it—do you want to screw up like that again because we learned not to do that.” - John Felushko (38:45) “There’s an old description of a product manager, like, ‘Oh, they come across as the smartest person in the room.’ Well, how do you become that person? Expand your view, and expand the amount of information you consume as widely as possible. That’s so important to UX design and thinking about what went wrong. Why are some customers super happy and some customers not? What is the difference between those two groups of people? Is it culture? Is it time? Is it mental ability? Is it the size of the screen they’re looking at my product on? What variables can I define and rule out, and what data sources do I have to answer all those questions? It’s just the normal product manager thing—constant curiosity.” -John Felushko (48:04)