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The AI landscape is evolving at breakneck speed, with new capabilities emerging quarterly that redefine what's possible. For professionals across industries, this creates a constant need to reassess workflows and skills. How do you stay relevant when the technology keeps leapfrogging itself? What happens to traditional roles when AI can increasingly handle complex tasks that once required specialized expertise? With product-market fit becoming a moving target and new positions like forward-deployed engineers emerging, understanding how to navigate this shifting terrain is crucial. The winners won't just be those who adopt AI—but those who can continuously adapt as it evolves. Tomasz Tunguz is a General Partner at Theory Ventures, a $235m early-stage venture capital firm. He blogs at tomtunguz.com & co-authored Winning with Data. He has worked or works with Looker, Kustomer, Monte Carlo, Dremio, Omni, Hex, Spot, Arbitrum, Sui & many others. He was previously the product manager for Google's social media monetization team, including the Google-MySpace partnership, and managed the launches of AdSense into six new markets in Europe and Asia. Before Google, Tunguz developed systems for the Department of Homeland Security at Appian Corporation. In the episode, Richie and Tom explore the rapid investment in AI, the evolution of AI models like Gemini 3, the role of AI agents in productivity, the shifting job market, the impact of AI on customer success and product management, and much more. Links Mentioned in the Show: Theory VenturesConnect with TomTom’s BlogGavin Baker on MediumAI-Native Course: Intro to AI for WorkRelated Episode: Data & AI Trends in 2024, with Tom Tunguz, General Partner at Theory VenturesRewatch RADAR AI  New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Important: Register on the event website to receive joining link. (rsvp on meetup will NOT receive joining link). If you can't make to the live session, still register to receive recordings.

Description: Join Monte Carlo for IMPACT 2025, our flagship virtual summit on data and AI observability. Hear from the most forward-thinking leaders as they share how to build resilient, trustworthy systems across the modern data and AI estate.

Whether you’re leading enterprise AI initiatives, managing large-scale data platforms, or tackling governance and compliance, IMPACT Virtual is your front-row seat to the next era of trusted data and AI. Join the half-day event to hear:

• - Real-world strategies for operationalizing observability across data and AI
• - Bridging the gap between data and AI in production
• - Lessons from top-performing organizations scaling AI with reliability
• - A first look at new innovations for the future of data + AI observability

Register on the event website to receive joining link. (rsvp on meetup will NOT receive joining link).

Important: Register on the event website to receive joining link. (rsvp on meetup will NOT receive joining link). If you can't make to the live session, still register to receive recordings.

Description: Join Monte Carlo for IMPACT 2025, our flagship virtual summit on data and AI observability. Hear from the most forward-thinking leaders as they share how to build resilient, trustworthy systems across the modern data and AI estate.

Whether you’re leading enterprise AI initiatives, managing large-scale data platforms, or tackling governance and compliance, IMPACT Virtual is your front-row seat to the next era of trusted data and AI. Join the half-day event to hear:

• - Real-world strategies for operationalizing observability across data and AI
• - Bridging the gap between data and AI in production
• - Lessons from top-performing organizations scaling AI with reliability
• - A first look at new innovations for the future of data + AI observability

Register on the event website to receive joining link. (rsvp on meetup will NOT receive joining link).

Important: Register on the event website to receive joining link. (rsvp on meetup will NOT receive joining link). If you can't make to the live session, still register to receive recordings.

Description: Join Monte Carlo for IMPACT 2025, our flagship virtual summit on data and AI observability. Hear from the most forward-thinking leaders as they share how to build resilient, trustworthy systems across the modern data and AI estate.

Whether you’re leading enterprise AI initiatives, managing large-scale data platforms, or tackling governance and compliance, IMPACT Virtual is your front-row seat to the next era of trusted data and AI. Join the half-day event to hear:

• - Real-world strategies for operationalizing observability across data and AI
• - Bridging the gap between data and AI in production
• - Lessons from top-performing organizations scaling AI with reliability
• - A first look at new innovations for the future of data + AI observability

Register on the event website to receive joining link. (rsvp on meetup will NOT receive joining link).

Important: Register on the event website to receive joining link. (rsvp on meetup will NOT receive joining link). If you can't make to the live session, still register to receive recordings.

Description: Join Monte Carlo for IMPACT 2025, our flagship virtual summit on data and AI observability. Hear from the most forward-thinking leaders as they share how to build resilient, trustworthy systems across the modern data and AI estate.

Whether you’re leading enterprise AI initiatives, managing large-scale data platforms, or tackling governance and compliance, IMPACT Virtual is your front-row seat to the next era of trusted data and AI. Join the half-day event to hear:

• - Real-world strategies for operationalizing observability across data and AI
• - Bridging the gap between data and AI in production
• - Lessons from top-performing organizations scaling AI with reliability
• - A first look at new innovations for the future of data + AI observability

Register on the event website to receive joining link. (rsvp on meetup will NOT receive joining link).

Important: Register on the event website to receive joining link. (rsvp on meetup will NOT receive joining link). If you can't make to the live session, still register to receive recordings.

Description: Join Monte Carlo for IMPACT 2025, our flagship virtual summit on data and AI observability. Hear from the most forward-thinking leaders as they share how to build resilient, trustworthy systems across the modern data and AI estate.

Whether you’re leading enterprise AI initiatives, managing large-scale data platforms, or tackling governance and compliance, IMPACT Virtual is your front-row seat to the next era of trusted data and AI. Join the half-day event to hear:

• - Real-world strategies for operationalizing observability across data and AI
• - Bridging the gap between data and AI in production
• - Lessons from top-performing organizations scaling AI with reliability
• - A first look at new innovations for the future of data + AI observability

Register on the event website to receive joining link. (rsvp on meetup will NOT receive joining link).

Important: Register on the event website to receive joining link. (rsvp on meetup will NOT receive joining link). If you can't make to the live session, still register to receive recordings.

Description: Join Monte Carlo for IMPACT 2025, our flagship virtual summit on data and AI observability. Hear from the most forward-thinking leaders as they share how to build resilient, trustworthy systems across the modern data and AI estate.

Whether you’re leading enterprise AI initiatives, managing large-scale data platforms, or tackling governance and compliance, IMPACT Virtual is your front-row seat to the next era of trusted data and AI. Join the half-day event to hear:

• - Real-world strategies for operationalizing observability across data and AI
• - Bridging the gap between data and AI in production
• - Lessons from top-performing organizations scaling AI with reliability
• - A first look at new innovations for the future of data + AI observability

Register on the event website to receive joining link. (rsvp on meetup will NOT receive joining link).

Data quality and AI reliability are two sides of the same coin in today's technology landscape. Organizations rushing to implement AI solutions often discover that their underlying data infrastructure isn't prepared for these new demands. But what specific data quality controls are needed to support successful AI implementations? How do you monitor unstructured data that feeds into your AI systems? When hallucinations occur, is it really the model at fault, or is your data the true culprit? Understanding the relationship between data quality and AI performance is becoming essential knowledge for professionals looking to build trustworthy AI systems. Shane Murray is a seasoned data and analytics executive with extensive experience leading digital transformation and data strategy across global media and technology organizations. He currently serves as Senior Vice President of Digital Platform Analytics at Versant Media, where he oversees the development and optimization of analytics capabilities that drive audience engagement and business growth. In addition to his corporate leadership role, he is a founding member of InvestInData, an angel investor collective of data leaders supporting early-stage startups advancing innovation in data and AI. Prior to joining Versant Media, Shane spent over three years at Monte Carlo, where he helped shape AI product strategy and customer success initiatives as Field CTO. Earlier, he spent nearly a decade at The New York Times, culminating as SVP of Data & Insights, where he was instrumental in scaling the company’s data platforms and analytics functions during its digital transformation. His earlier career includes senior analytics roles at Accenture Interactive, Memetrics, and Woolcott Research. Based in New York, Shane continues to be an active voice in the data community, blending strategic vision with deep technical expertise to advance the role of data in modern business. In the episode, Richie and Shane explore AI disasters and success stories, the concept of being AI-ready, essential roles and skills for AI projects, data quality's impact on AI, and much more. Links Mentioned in the Show: Versant MediaConnect with ShaneCourse: Responsible AI PracticesRelated Episode: Scaling Data Quality in the Age of Generative AI with Barr Moses, CEO of Monte Carlo Data, Prukalpa Sankar, Cofounder at Atlan, and George Fraser, CEO at FivetranRewatch RADAR AI  New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Title: AI-Powered Observability for Cloud-Native Systems: Leveraging Large Language Models in Platform-as-a

YouTube Live: https://youtube.com/live/jMhcqbixl58?feature=share

Session Description: As global investment in public cloud services is projected to surpass $723 billion by 2025, organizations are rapidly shifting toward microservices-based, cloud-native platforms. Yet, conventional observability tools often struggle to deliver actionable insights in these dynamic, distributed environments slowing down diagnostics, root cause analysis, and performance optimization. This session presents a transformative approach to observability by embedding Large Language Models (LLMs) into Platform-as-a-Service (PaaS) ecosystems, enabling intelligent automation and deeper visibility across the application stack. Attendees will explore a real-world financial services case study where LLM integration led to a 43% reduction in mean time to resolution (MTTR), 57% decrease in false positives, and a 70% boost in developer productivity through intelligent code refactoring. Using a mixed-methods evaluation strategy combining telemetry data, qualitative insight analysis, and statistically validated A/B testing the talk demonstrates clear gains in system efficiency (25%), failure reduction (28%), and customer satisfaction (18%) post-migration. Advanced simulations also reveal a projected 3.4x return on investment over three years using Monte Carlo techniques. The presentation offers a practical, modular framework for integrating LLMs into existing observability pipelines, with strategies to address real-world challenges including privacy, explainability, and model generalization. Topics such as federated learning, confidence scoring, and ethical AI design principles will be explored. Ideal for cloud architects, DevOps engineers, and platform leaders, this session bridges technical depth with business value charting a path toward self-healing, multimodal observability, and domain-specific LLMs for enterprise-grade resilience.

Be Ready to Engage:

• Bring a notepad – this is a content-rich session!
• Submit your questions beforehand via Comments.
• Ask questions live via comments or join the stage with camera/mic access – just reach out to us in advance if you’d like to participate on screen.

Email: [email protected] LinkedIn: https://www.linkedin.com/company/microsoft-user-group-uttar-pradesh-power-bi-club/ WhatsApp Community: https://chat.whatsapp.com/C7jqrw5ZTkl9tledkqqVu7

Attending Big Data London next week? Don’t miss AI After Hours—the exclusive after party across from Olympia.

After a full first day at the show, join Striim, Monte Carlo, Snowplow, and Dagster for AI After Hours – The Exclusive After Party.

Expect an evening of relaxed networking, lively conversation, classic pub fare, and plenty of drinks—all in one of London’s most iconic pubs. This is your chance to connect with fellow data and AI leaders, swap insights, and wind down just steps from the conference.

⚠️ Important Registration info: To attend the meetup, you’ll need to register for a free ticket to Big Data LDN.

👉 Register here for your free pass.

While it’s totally fine to just show up for the meetup itself, registering for Big Data LDN gives you access to:

• A packed agenda of keynotes, breakout sessions, and panels across the data and AI landscape.
• A chance to explore the exhibition floor with 180+ leading tech companies.
• Networking opportunities with thousands of data professionals and industry leaders.

Striim is exhibiting at booth P80 at Big Data LDN this year, so we hope you will come and see us on the expo floor during the day to have a chat with the team and grab some exclusive swag!

See you there!

As the pioneers of the low-code market since 2001, enterprise software delivery solution OutSystems has evolved rapidly alongside the changing landscape of data. With a global presence and a vast community of over 750,000 members, OutSystems continues to leverage innovative tools, including data observability and generative AI, to help their customers succeed.

In this session, Pedro Sá Martins, Head of Data Engineering, will share the evolution of OutSystems’ data landscape, including how OutSystems has partnered with Snowflake, Fivetran and Monte Carlo to address their modern data challenges. He’ll share best practices for implementing scalable data quality programs to drive innovative technologies, as well as what’s on the data horizon for the OutSystems team.

Put statistics into practice with Python! Data-driven decisions rely on statistics. Statistics Every Programmer Needs introduces the statistical and quantitative methods that will help you go beyond “gut feeling” for tasks like predicting stock prices or assessing quality control, with examples using the rich tools of the Python ecosystem. Statistics Every Programmer Needs will teach you how to: Apply foundational and advanced statistical techniques Build predictive models and simulations Optimize decisions under constraints Interpret and validate results with statistical rigor Implement quantitative methods using Python In this hands-on guide, stats expert Gary Sutton blends the theory behind these statistical techniques with practical Python-based applications, offering structured, reproducible, and defensible methods for tackling complex decisions. Well-annotated and reusable Python code listings illustrate each method, with examples you can follow to practice your new skills. About the Technology Whether you’re analyzing application performance metrics, creating relevant dashboards and reports, or immersing yourself in a numbers-heavy coding project, every programmer needs to know how to turn raw data into actionable insight. Statistics and quantitative analysis are the essential tools every programmer needs to clarify uncertainty, optimize outcomes, and make informed choices. About the Book Statistics Every Programmer Needs teaches you how to apply statistics to the everyday problems you’ll face as a software developer. Each chapter is a new tutorial. You’ll predict ultramarathon times using linear regression, forecast stock prices with time series models, analyze system reliability using Markov chains, and much more. The book emphasizes a balance between theory and hands-on Python implementation, with annotated code and real-world examples to ensure practical understanding and adaptability across industries. What's Inside Probability basics and distributions Random variables Regression Decision trees and random forests Time series analysis Linear programming Monte Carlo and Markov methods and much more About the Reader Examples are in Python. About the Author Gary Sutton is a business intelligence and analytics leader and the author of Statistics Slam Dunk: Statistical analysis with R on real NBA data. Quotes A well-organized tour of the statistical, machine learning and optimization tools every data science programmer needs. - Peter Bruce, Author of Statistics for Data Science and Analytics Turns statistics from a stumbling block into a superpower. Clear, relevant, and written with a coder’s mindset! - Mahima Bansod, LogicMonitor Essential! Stats and modeling with an emphasis on real-world system design. - Anupam Samanta, Google A great blend of theory and practice. - Ariel Andres, Scotia Global Asset Management

Shane Murray (Field CTO Monte Carlo, Former Head of Data NY Times) joins me to chat about the impact of AI on data teams and business strategies, data observability on unstructured data, and more.

OpenMC is an open source, community-developed, Monte Carlo tool for neutron transport simulations, featuring a depletion module for fuel burnup calculations in nuclear reactors and a Python API. Depletion calculations can be expensive as they require solving the neutron transport and bateman equations in each timestep to update the neutron flux and material composition, respectively. Material properties such as temperature and density govern material cross sections, which in turn govern reaction rates. The reaction rates can effect the neutron population. In a scenario where there is no significant change in the material properties or composition, the transport simulation may only need to be run once; the same cross sections are used for the entire depletion calculation. We recently extended the depletion module in OpenMC to enable transport-independent depletion using multigroup cross sections and fluxes. This talk will focus on the technical details of this feature, its validation, and briefly touch on areas where the feature has been used. Two recent use cases will be highlighted. The first use case calculates shutdown dose rates for fusion power applications, and the second performs depletion for fission reactor fuel cycle modeling.

In our May 2025 meetup, we explore the interdisciplinary application of data science in the biopharmaceutical industry. Christina Yassouridis, Senior Data Scientist from Boehringer Ingelheim, will share with us how she designs and develops an R package which applies Design of Experiment (DoE) approaches to optimize biopharmaceutical manufacturing process.

Expect to hear about: 🟡 Data science: Theoretical background of advanced Design of Experiments (DoE) approach using Halton designs and Latin Hypercube Designs 🟣 R programming: The design and development of R package to meet industry needs 🟢 Biopharmaceutical industry: Current industry applications of R package in solving optimization problems

It will be an interesting sharing to see how R is applied to solve real-world problems in biopharmaceutical manufacturing, an area closely connected to our well-being. The theoretical background of the DoE approaches involved will also be introduced, which allows R users without field knowledge to follow along.

R-Ladies Vienna promotes gender diversity in the R community. All genders and skill levels in R are welcome to our events. We look forward to seeing you!

🗣️ Speaker Christina is currently a Senior Data Scientist at Boehringer Ingelheim, one of the world’s largest pharmaceutical companies. With a PhD in Applied Statistics, Christina has extensive experience as a data science professional in both industry and consulting, and as a researcher at BOKU.

ℹ️ Abstract Biopharmaceutical manufacturing encompasses complex processes involving living organisms and multiple interacting steps. To optimize these processes, data scientists develop digital representations using machine learning models to identify optimal process parameters across multidimensional experimental spaces. For Gaussian process regression models, evenly distributed experimental points yield superior results.

The R package {spacefillngDoEs}, developed by Christina, integrates and extends two established approaches:

1. Halton designs: These low-discrepancy sequences enable efficient quasi-Monte Carlo methods for numerical integration and sampling in high-dimensional spaces.
2. Latin Hypercube Designs: This statistical method generates near-random parameter value samples from multidimensional distributions.

By combining functionalities from the R packages {qrng} and {SLHD} while extending their capabilities to handle mixed data types, {spacefillngDoEs} addresses specific industry requirements.

In this presentation, Christina will introduce the theoretical foundations of generalized Halton designs, demonstrate the R package's functionality, and showcase its current industrial application.

⏱️ Duration The event will last approximately 1 to 1.5 hours, including a Q&A session. Doors open at 17:45.

🗺️ Location Seminar Room DA green 04 / Seminarraum DA grün 04 (DA04E10) 4th floor, green area TU Wien Freihaus (map) Wiedner Hauptstraße 8-10, 1040 Wien

📍 How to find us In the green area of TU Wien Freihaus, take the lift to the 4th floor. After exiting the lift, turn left to enter through the green doors, then turn left again. The seminar room will be on your left. Use this floor plan to help locate the room.