REGISTER BELOW FOR MORE AVAILABLE DATES! ↓↓↓↓↓ https://luma.com/stelios

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Who is this for?

​Students, developers, and anyone interested in using Large Language Models (LLMs) to build real software solutions with ** Python.

​Tired of vibe coding with AI tools? Want to actually understand and own your code, instead of relying on black-box magic? This session shows you how to build LLM systems properly, with full control and clear engineering principles. ​ Who is leading the session?

​The session is led by Dr. Stelios Sotiriadis, CEO of Warestack, Associate Professor and MSc Programme Director at Birkbeck, University of London, specialising in cloud computing, distributed systems, and AI engineering.

​Stelios holds a PhD from the University of Derby, completed a postdoctoral fellowship at the University of Toronto, and has worked on industry and research projects with Huawei, IBM, Autodesk, and multiple startups. Since moving to London in 2018, he has been teaching at Birkbeck. In 2021, he founded Warestack, building software for startups around the world. ​ What we’ll cover?

​A hands-on introduction to building software with LLMs using Python, Ollama, and LiteLLM, including:

• ​How LLMs, embeddings, and agents work.
• ​Calling local models with Ollama or cloud models (OpenAI, Gemini and more).
• ​Using LiteLLM for custom prompts and tool-calling.
• ​Building simple agents from scratch.
• ​Introduction to RAG (Retrieval-Augmented Generation).
• ​Working with vector databases (ChromaDB) and vector similarity search library (FAISS).
• ​Storing, searching, and retrieving embeddings.
• ​Introduction to Streamlit for interactive data apps.
• ​End-to-end examples you can run on your own machine.

​This session focuses on theory, fundamentals and real code you can re-use.

​Why LiteLLM?

​LiteLLM gives you low-level control to build custom LLM solutions your own way, without a heavy framework like LangChain, so you understand how everything works and design your own architecture. A dedicated LangChain session will follow for those who want to go further.

​What are the requirements?

​Bring a laptop with Python installed (Windows, macOS, or Linux), along with Visual Studio Code or a similar IDE, with at least 10GB of free disk space and 8GB of RAM.

​This space is needed for running local models during the workshop. ​If you don’t have a suitable laptop, please contact Stelios ([email protected]) before registering.

​What is the format?

​A 3-hour live session with:

• ​Interactive theory blocks
• ​Hands-on coding
• ​Step-by-step exercises
• ​Small group support
• ​Three 10-minute breaks
• ​Q&A and class quizzes

​This is a highly practical, hands-on class focused on code and building working LLM systems.

​What are the prerequisites?

​A good understanding of programming with Python is required (basic to intermediate level). I assume you are already comfortable writing Python scripts.

​What comes after?

​Participants will receive an optional mini capstone project with one-to-one personalised feedback.

​Is it just one session?

​This is the first session in a new sequence on applied AI, covering agents, RAG systems, vector databases, and production-ready LLM workflows. Later sessions will dive deeper into topics such as embeddings with deep neural networks, LangChain, advanced retrieval, and multi-agent architectures.

​You can decide afterwards whether you’d like to join future sessions.

​How many participants?

​To keep this interactive, only 15 spots are available. Please register as soon as possible.

Description Microsoft Fabric is powerful, but without the right monitoring and governance in place, it can quickly become a black box of ballooning costs, failed refreshes, and unclear ownership. In this interactive session, we’ll break down: The real-world governance challenges teams face in Microsoft Fabric What Microsoft offers out-of-the-box and where it falls short How to monitor your data estate, refreshes, and usage in a meaningful way. Proven strategies to track, forecast costs, audit, and optimise your Fabric environment. We’ll also showcase how you can enable data teams to: - Score their governance maturity - Monitor all of your capacities\, refresh and workspace activity in real-time - Get AI-powered recommendations to improve platform performance - Make informed decisions across cost\, access\, and risk Whether you’re a CTO, BI lead, platform owner, or Power BI pro, this session will equip you with practical tools and a smarter mindset to take full control of your Fabric environment. Bonus: Live demo of governance scoring and AI-powered Fabric optimisation monitoring in action.

Don’t miss your chance to register for the Future Data Driven Summit 2025, over 55 expert speakers are lined up to inspire and inform!

Venue: Riverbank House, 2 Swan Ln, London EC4R 3AD

Please note:

1. 🚨🚨🚨A valid photo ID is required by building security. 🚨🚨🚨
2. This event follows the NumFOCUS Code of Conduct. Please familiarise yourself with it before attending.

If your RSVP status says "You're going" you will be able to get in. No need to show your RSVP confirmation when signing in. If you can no longer make it, please unRSVP as soon as possible. *** Code of Conduct: This event follows the NumFOCUS Code of Conduct. Please get in touch with the organisers with any questions or concerns. *** As always, there will be free food and drinks, generously provided by our host, Man Group. ***

Main Talks

1. Skrub: Machine Learning with DataFrames - Gaël Varoquaux

While data-science often talks about machine learning, much of the work lies in preparing and assembling DataFrames - a process that is highly manual. I'll introduce Skrub, a young package that eases machine learning with DataFrames. It provides a variety of tools to plug any scikit-learn-type model into complex and messy DataFrames with no manual effort. I will also discuss the exciting "DataOps" features coming in the new release, which wrap and record any data assembly or wrangling pipeline, and can apply full machine-learning workflows: applying the plan on new data, cross-validation, or tuning it to maximise prediction accuracy on a task.

2. Breaking the Black Box - How to Evaluate Your Agents... in Real Time Too! - Craig West

If you are building with LLMs, creating high quality evaluations is one of the most impactful things you can do. Without evals, it can be very difficult and time intensive to understand how different model versions might affect your use case. This talk aims to provide you a roadmap that may be simpler than you think to implement.

In this talk, we will look at the two aspects of Observability and Evaluation. Using the manual evaluating-ai-agents.com, along with its code repo, we will see that observability can be done without vendor solutions but with standard Python, either during Evaluation Driven Development or after development.

We will look at three core evaluation strategies - deterministic, human and LLM as Judge - with code examples.

⚡ Lightning Talks

1. From RNNs to Reliable Agents: Context Engineering, Roles, and What 1M-Token Windows Don’t Fix - Imamuddin Shaik
2. The Apprenticeship Pathway Into Data Science - Ruby Waller

Logistics Doors open at 6.30 pm (get there early as you'll need to sign in with building security). Talks start at 7:00 pm, with drinks afterwards from 9:00 pm at The Banker (EC4).

We have reduced capacity for this event, but there will be plenty of people to discuss data science questions with!

Please unRSVP in good time if you realise you can't make it. We're limited by building security on the number of attendees, so please free up your place for your fellow community members!

Send us a text The AI Advantage: Get Better Results from LLMs with the Perfect Prompt On this episode of Making Data Simple, we’re joined by Jonathan Mast, AI consultant and coach at Whitebeard Strategies and creator of the Perfect Prompting Framework™. Jonathan’s not just riding the AI wave—he’s teaching business leaders and everyday users how to surf it, with simple, actionable tools that unlock meaningful results from large language models. If you've ever stared at a prompt box wondering what to type—or worse, gotten garbage back from AI—this episode is for you. We talk about what works, what doesn’t, and what’s coming next (agents, anyone?). Plus, Jonathan breaks down his 4-step framework that’s helping 300K+ community members and clients scale AI with clarity and confidence. ⏱️ Episode Timestamps 01:34 Introducing Jonathan Mast04:13 Digital Agency05:29 Whitebeard Strategies08:06 ADD09:57 Back to Whitebeard14:51 The Perfect Prompting Framework21:36 The Four Step Method24:58 What if You Don't Use AI?28:37 Agents30:08 Whitebeard Engagements32:42 Getting Started36:39 What's True But Not a Consensus?37:23 For Fun🔗 Connect with Jonathan LinkedIn: https://www.linkedin.com/in/jonathanjmast/Website: https://whitebeardstrategies.com#MakingDataSimple #PerfectPromptingFramework #AIforBusiness #AIProductivity #JonathanMast #PromptEngineering #LLMs #AIAgents #WhitebeardStrategies #TechPodcast #DataSimplified Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Hello Testers, With great pleasure, we announce the upcoming event of our

TTTribeCast Webinar series , “Securing LLMs: Insights into OWASP top 10” by Maryia Tuleika ( Quality Engineering Lead at Regent AB ) .

When is it happening? 16th May 2025 \| Friday \| 3 PM CET

What will Maryia speak about: "What if I told you that you can trick an LLM into revealing secrets, making bad choices or even acting against its own rules? AI may seem like a black box, but when you start testing it like any other system, surprising weaknesses start to appear.

The good news? You don’t need to reinvent the wheel to test AI. Strong system thinking, traditional testing techniques, and a critical mindset are already powerful tools for uncovering vulnerabilities. The same skills used to break and improve software (like exploratory testing, risk analysis, extensive logging and monitoring) can help make AI systems safer and more predictable."

About Maryia Tuleika : Maryia is a Quality Engineering Leader with a strong focus on backend testing and embedded systems. She leads testing initiatives, drives education programs and actively contributes to the Swedish testing community. As a mentor, speaker and content creator on LinkedIn, she helps new test professionals build their skills and confidence. With experience spanning multiple industries, she brings a broad perspective on testing, prioritization and risk-based decision-making. She believes that great testing is about both technical excellence and knowing when to step back, think critically and enjoy the process.

FAQs: A) Where can I join the online community? => Join thousands of other Testers here- bit.ly/tttdiscord B) Which platform would be used for this online Meet-up? => We will be using Hubilo, and the joining link will be shared over an email close to the event date.

What is TTTribeCast?

TTTribeCast is the Webinar Series by The Test Tribe where any Tester from the World can take up the virtual stage and share his/her knowledge with our Tribe.

The format is simple. The speaker goes live, and shares his/her knowledge on the decided topic, followed by a quick Q&A through comments on the Live Video.

About The Test Tribe: The Test Tribe is the World’s Largest Software Testing Community turned EdTech Startup. Started in 2018 with a mission to give Testing Craft the glory it deserves while we co-create Smarter, prouder, and confident Testers. We take pride in solving upskilling and growth for global Testing professionals through our unique offerings like Expert Courses, Membership, Cohorts, Offline Mixers, online Community spaces, and a lot of global Events. Our offerings enable Software Testers globally to collaborate, learn, and grow together. With around 400+ Software Testing Events like Conferences, Hackathons, Meetups, Webinars, etc., and with other Community initiatives, we have reached a global footprint of over 120K+ Testers from 130+ Countries. We intend to provide life-altering growth to every single Testing professional on the planet through community and technology.

Join thousands of other Testers in the community. Discord Community See you all at Live Hour. Happy Learning! Happy Testing!

Data and AI are shifting industries and reshaping society, but with great power comes the need for robust governance. In this session, we will focus on governance in data and AI, highlighting why it is a strategic priority for organizations worldwide.

Join this session to discover how trusted AI governance with SAS Viya ensures transparency, ethics, and performance—without slowing innovation. Walk away with real-world strategies to keep your AI trustworthy, efficient, and impactful. Don’t let bad AI decisions cost you.

RLadies Ottawa invites you to our third annual data hackathon to celebrate International Women's day!

At our posit::conf(2024) watch party we watched “GitHub: How To Tell Your Professional Story” by Abigail Haddad. In this presentation Haddad highlighted the importance of building a compelling portfolio that not only highlights your skills but explains your professional identity.

For this year’s data challenge we encourage you to find YOUR passion project to round out your portfolio and develop your professional story! Take a look at your GitHub profile and ask yourself, what type of project is missing here?

For example, if you participated in last year’s challenge and created a traditional plot of some sort, this year try creating a visualization using a table, dashboard or another-outside-the-box presentation tool.

You can find the full details on our website.

For more inspo, be sure to check out our upcoming in-person event Coding for a Cause with Claudie Larouche on March 18th.

Guidelines

Although this challenge is hosted by R-Ladies Ottawa, you don’t have to use R to participate. You could use R, Python, a combination of the two, or anything you’re comfortable with!

No previous programming experience is required! If you’re interested in participating, but aren’t sure where to start, check out the recordings from our Introduction to R workshops that we hosted in early 2024. The workshops will give you a solid foundation in data analysis and data visualization using R.

While anyone is welcome to participate in the challenge and submit an entry, only submissions by women and gender minorities who reside in Canada will be eligible for the prizes.

You can participate individually, or in teams of up to 5 people!

The most creative submission, chosen by the organizing team, will receive a $25 virtual gift card to Maker House, generously donated by them. Maker House offers a curated collection of gifts and homewares, crafted locally in Ottawa and Canada 🍁

How to submit an entry

This will be a month-long data challenge, and you’ll have until April 4th to submit your entry. Your submission can be in any form you like – it can be a Quarto document, an image, a link to a webpage, or a link to a video, for example.

There are two options for submitting an entry:

• Option 1 (preferred option): If you have experience with GitHub and Quarto, you can create a pull request on this repository and add your submission to the “Submissions” part of this page
• Option 2: If you don’t have experience with GitHub, you can email [email protected] a link to your submission materials and we will add the link to our website.

After the submission deadline, R-Ladies Ottawa will host a virtual event where participants can showcase their work. This is a great way to learn from one another and to connect with other like-minded individuals!

The date and time of the showcase event will be announced soon.

Global AI Bootcamp taking place in London. If you are an AI enthusiast and love learning more about Azure AI, then this event is perfect for you.

09:30 - 09:45 Welcoming (15 mins)

09:45 - 10:20 Speaker 1 - Opening Keynote - Amy Boyd - Senior Cloud Developer Advocate in AI/ML at Microsoft

Session: To be Confirmed

10:20 - 10:45 Speaker 2 - Nathaniel Okenwa (He/Him) Developer Evangelist @twilio / Wannabe Dev-Rel Superstar / Loves: Coding

Session: Preventing Skynet: Managing Trust with AI Agents

In this talk, we’ll explore the practical dangers of AI agent infrastructure, discuss strategies to safeguard your systems, and provide a comprehensive safety checklist to help you build responsibly. Together, we can prevent the singularity. Remember: The future is not set. There is no fate but what we make for ourselves.

10:45 - 11:10 Speaker 3 - Rahul Basu - Senior Software Engineer at Warner Bros. Discovery

Session: AI, Hollywood, and the Metaverse – A Synergistic Future

Artificial intelligence is set to become the backbone of the metaverse, merging physical and digital realities into immersive experiences. This session explores how AI is transforming storytelling, character creation, and interactive content, revolutionizing the entertainment industry. From generative AI-powered narratives to real-time digital actors, we’ll examine how Hollywood and gaming companies are leveraging AI to redefine engagement. Drawing from his experience architecting Max, Warner Bros. Discovery’s global streaming platform, Rahul Basu will share insights into how AI is being used by top media companies to power next-gen content creation. Attendees will learn about AI-driven MLOps frameworks, synthetic media production, and the tools shaping the future of the entertainment industry. Whether you’re a developer, AI enthusiast, creative, or media executive, this session will provide practical knowledge and real-world use cases on how AI is reshaping the future of entertainment.

11:10 - 11:30 Break (15 mins)

11:30 - 12:15 Speaker 4 - Guy Gregory - Partner Solution Architect@Microsoft specialising in Azure AI.

Session: Introducing Azure AI Agent Service

In this session, we’ll introduce the Azure AI Agent Service, a set of feature-rich, managed capabilities that brings together all the models, data, tools, and services that partners and customers need to automate business processes of any complexity. Here’s what we’ll cover:

• Introducing the Azure AI Agent Service
• How to use the Azure AI Agent Service via SDK and Azure AI Foundry
• How does the Azure AI Agent Service compare to Chat Completions and Assistants API?
• ‘What’s in the box?’ Models, Tools, Actions, and Security
• Creating your Agent Project, and deploying your model
• Creating a basic agent
• Creating an agent with tools
• Advanced agents / Multi-agent examples
• Further reading and training

12:15 - 13:00 Speaker 5 - Manuel Sanchez Rodriguez Director - Azure App Modernisation at NTT DATA \| Microsoft MVP Azure & AI Platform and Sergio Sisternes Senior Director - Head of Cloud Native Solutions at NTT DATA

Session: The Age of Agents: Modernising Your Applications

Join us for an exciting session, "The Age of Agents: Modernising Your Applications" with Azure and Azure OpenAI, where you'll discover how to transform and modernise your applications using the powerful capabilities of Azure and Azure OpenAI. Learn how intelligent agents can enhance performance and user experience, explore practical demonstrations of seamless integration, and get a comprehensive guide on overcoming common challenges. Whether you're a developer, IT professional, or tech enthusiast, this session will equip you with the knowledge and tools to stay ahead in the rapidly evolving tech landscape. Don't miss this opportunity to unlock your applications' full potential with Azure and Azure OpenAI.

13:00 - 14:00 Lunch Break (

14:00 - 14:45 Speaker 6 - Clifford Agius - Freelance Developer that just so happens to fly an Airbus A320 around the Europe.

Session: Could a Co-Pilot be an Aircraft Co-Pilot

Welcome aboard, fellow travelers! In this talk, we’re strapping on our metaphorical aviator goggles and diving into the cockpit to explore the fascinating world of AI Co-Pilots and could they and more importantly should they be used in a real world flight deck? Buckle up, because this flight is about to get bumpy—in a purely intellectual turbulence kind of way. Using aircraft incidents and accidents we will see if an AI Co-Pilot would have saved the day... If you have a fear of flying maybe skip this one but I will say before you run out that none of the accidents are fatal so nothing to be scared of.... You don't need to be an AI specialist or even know anything about it, as we are just exploring the decisions pilots have to make in very limited timescales with limited information and throwing in some human factors to spice it up a bit. These are all good skills you can use in any decision making at work. The aim of this session is to hopefully show you that AI can help but should we use it? You will walk away knowing a bit about AI and have some fun geeking out on aviation.

14:45 - 15:10 Speaker 7 - Ayca Bas - Senior Cloud Developer Advocate@Microsoft

Session: RAG and Knowledge Retrieval Fundamentals

In this session, we are going to talk about what is Retrieval Augmented Generation and why It’s a Game Changer. We'll also learn the fundamentals of search indexing and talk about Vector Search 101: Revolutionizing Retrieval with AI.

15:10 - 15:55 Speaker 8 Dieter Gobeyn - Enterprise Integration Architect & Azure Cloud Solutions Architect

Session: Azure OpenAI Unplugged: Real-World Lessons from My Latest GenAI Project

Thinking of using Azure OpenAI in production? In this session, I’ll share detailed hard-earned lessons from my latest GenAI project, where I combined Azure OpenAI GPT-4 with data integration workflows. As both a solution architect and hands-on developer, I’ll walk you through what worked, what flopped, and what I wish I knew earlier. We’ll dive into managing costs, securing data, reducing hallucinations, fine-tuning prompts, and keeping token usage in check. Whether you’re exploring AI or scaling it, this session is packed with tips to help you avoid pitfalls and build smarter, more efficient AI solutions.

16:00pm - Closing Keynote Lee Stott - Principal Cloud Advocate Manager@Microsoft

Session: Device Intelligence with SLM: A Path to Edge Deployment and Cloud Integration

In today’s fast-paced tech landscape, many organisations aim to enhance device intelligence and responsiveness. This presentation highlights the crucial role of Small Language Models (SLM) in solving business problems and improving device capabilities. By fine-tuning SLM, you can achieve optimal performance and adaptability. We will compare SLM with Large Language Models (LLM), showing why SLM is better suited for organisation’s needs. The importance of customization in fine-tuning these models will be emphasized. A key focus will be on deploying these models on edge devices for real-time processing and decision-making, boosting device intelligence and positioning your organisation as an innovation leader. The presentation will provide a roadmap for implementation, from cloud development to deployment across platforms. Using Azure, organisations can ensure seamless integration and IP ownership, gaining a competitive edge.

Discover how your organisation can leverage SLM to revolutionize its devices and lead in intelligent technology.

By registering for this in person event you are agreeing to have your e-mail shared with building security for building access. Your privacy is important to us. This privacy statement explains the personal data Microsoft processes, how Microsoft processes it, and for what purposes. You can view our full privacy statement here: https://privacy.microsoft.com/privacystatement

Are you prepared for the hidden UX taxes that AI and LLM features might be imposing on your B2B customers—without your knowledge? Are you certain that your AI product or features are truly delivering value, or are there unseen taxes that are working against your users and your product / business? In this episode, I’m delving into some of UX challenges that I think need to be addressed when implementing LLM and AI features into B2B products.

While AI seems to offer the change for significantly enhanced productivity, it also introduces a new layer of complexity for UX design. This complexity is not limited to the challenges of designing in a probabilistic medium (i.e. ML/AI), but also in being able to define what “quality” means. When the product team does not have a shared understanding of what a measurably better UX outcome means, improved sales and user adoption are less likely to follow. 

I’ll also discuss aspects of designing for AI that may be invisible on the surface. How might AI-powered products change the work of B2B users? What are some of the traps I see some startup clients and founders I advise in MIT’s Sandbox venture fund fall into?

If you’re a product leader in B2B / enterprise software and want to make sure your AI capabilities don’t end up creating more damage than value for users,  this episode will help!  

Highlights/ Skip to 

Improving your AI model accuracy improves outputs—but customers only care about outcomes (4:02) AI-driven productivity gains also put the customer’s “next problem” into their face sooner. Are you addressing the most urgent problem they now have—or used to have? (7:35) Products that win will combine AI with tastefully designed deterministic-software—because doing everything for everyone well is impossible and most models alone aren’t products (12:55) Just because your AI app or LLM feature can do ”X” doesn't mean people will want it or change their behavior (16:26) AI Agents sound great—but there is a human UX too, and it must enable trust and intervention at the right times (22:14) Not overheard from customers: “I would buy this/use this if it had AI” (26:52) Adaptive UIs sound like they’ll solve everything—but to reduce friction, they need to adapt to the person, not just the format of model outputs (30:20) Introducing AI introduces more states and scenarios that your product may need to support that may not be obvious right away (37:56)

Quotes from Today’s Episode

Product leaders have to decide how much effort and resources you should put into model improvements versus improving a user’s experience. Obviously, model quality is important in certain contexts and regulated industries, but when GenAI errors and confabulations are lower risk to the user (i.e. they create minor friction or inconveniences), the broader user experience that you facilitate might be what is actually determining the true value of your AI features or product. Model accuracy alone is not going to necessarily lead to happier users or increased adoption. ML models can be quantifiably tested for accuracy with structured tests, but because they’re easier to test for quality vs. something like UX doesn’t mean users value these improvements more. The product will stand a better chance of creating business value when it is clearly demonstrating it is improving your users’ lives. (5:25) When designing AI agents, there is still a human UX - a beneficiary - in the loop. They have an experience, whether you designed it with intention or not. How much transparency needs to be given to users when an agent does work for them? Should users be able to intervene when the AI is doing this type of work?  Handling errors is something we do in all software, but what about retraining and learning so that the future user experiences is better? Is the system learning anything while it’s going through this—and can I tell if it’s learning what I want/need it to learn? What about humans in the loop who might interact with or be affected by the work the agent is doing even if they aren’t the agent’s owner or “user”? Who’s outcomes matter here? At what cost? (22:51) Customers primarily care about things like raising or changing their status, making more money, making their job easier, saving time, etc. In fact,I believe a product marketed with GenAI may eventually signal a negative / burden on customers thanks to the inflated and unmet expectations around AI that is poorly implemented in the product UX. Don’t think it’s going to be bought just because it using  AI in a novel way. Customers aren’t sitting around wishing for “disruption” from your product; quite the opposite. AI or not, you need to make the customer the hero. Your AI will shine when it delivers an outsized UX outcome for your users (27:49) What kind of UX are you delivering right out of the box when a customer tries out your AI product or feature? Did you design it for tire kicking, playing around, and user stress testing? Or just an idealistic happy path? GenAI features inside b2b products should surface capabilities and constraints particularly around where users can create value for themselves quickly.  Natural hints and well-designed prompt nudges in LLMs for example are important to users and to your product team: because you’re setting a more realistic expectation of what’s possible with customers and helping them get to an outcome sooner. You’re also teaching them how to use your solution to get the most value—without asking them to go read a manual. (38:21)

Summary In this episode of the Data Engineering Podcast Dan Bruckner, co-founder and CTO of Tamr, talks about the application of machine learning (ML) and artificial intelligence (AI) in master data management (MDM). Dan shares his journey from working at CERN to becoming a data expert and discusses the challenges of reconciling large-scale organizational data. He explains how data silos arise from independent teams and highlights the importance of combining traditional techniques with modern AI to address the nuances of data reconciliation. Dan emphasizes the transformative potential of large language models (LLMs) in creating more natural user experiences, improving trust in AI-driven data solutions, and simplifying complex data management processes. He also discusses the balance between using AI for complex data problems and the necessity of human oversight to ensure accuracy and trust.

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. As a listener of the Data Engineering Podcast you clearly care about data and how it affects your organization and the world. For even more perspective on the ways that data impacts everything around us don't miss Data Citizens® Dialogues, the forward-thinking podcast brought to you by Collibra. You'll get further insights from industry leaders, innovators, and executives in the world's largest companies on the topics that are top of mind for everyone. In every episode of Data Citizens® Dialogues, industry leaders unpack data’s impact on the world; like in their episode “The Secret Sauce Behind McDonald’s Data Strategy”, which digs into how AI-driven tools can be used to support crew efficiency and customer interactions. In particular I appreciate the ability to hear about the challenges that enterprise scale businesses are tackling in this fast-moving field. The Data Citizens Dialogues podcast is bringing the data conversation to you, so start listening now! Follow Data Citizens Dialogues on Apple, Spotify, YouTube, or wherever you get your podcasts.Your host is Tobias Macey and today I'm interviewing Dan Bruckner about the application of ML and AI techniques to the challenge of reconciling data at the scale of businessInterview IntroductionHow did you get involved in the area of data management?Can you start by giving an overview of the different ways that organizational data becomes unwieldy and needs to be consolidated and reconciled?How does that reconciliation relate to the practice of "master data management"What are the scaling challenges with the current set of practices for reconciling data?ML has been applied to data cleaning for a long time in the form of entity resolution, etc. How has the landscape evolved or matured in recent years?What (if any) transformative capabilities do LLMs introduce?What are the missing pieces/improvements that are necessary to make current AI systems usable out-of-the-box for data cleaning?What are the strategic decisions that need to be addressed when implementing ML/AI techniques in the data cleaning/reconciliation process?What are the risks involved in bringing ML to bear on data cleaning for inexperienced teams?What are the most interesting, innovative, or unexpected ways that you have seen ML techniques used in data resolution?What are the most interesting, unexpected, or challenging lessons that you have learned while working on using ML/AI in master data management?When is ML/AI the wrong choice for data cleaning/reconciliation?What are your hopes/predictions for the future of ML/AI applications in MDM and data cleaning?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 TamrMaster Data ManagementCERNLHCMichael StonebrakerConway's LawExpert SystemsInformation RetrievalActive LearningThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

We are happy to host this meetup together with the London Java Community.

As always, this event will allow for insightful discussions on the latest in Graph Technology. Don't miss this opportunity to connect with fellow Java enthusiasts and explore cutting-edge developments in Java technology.

Agenda 18:00 - 18:30 Welcome/Snacks 18:30 - 19:00 Session 1: Simon Maple 19:00 - 19:30 Session 2: Gerrit Meier 19:30 - 19:45 Q&A 19:45 Networking/Drinks

Session 1: On the Path to AI Native Development: Live coding applications with Attended and Autonomous AI Dev Tools - Simon Maple\, Founding Developer Relations from Tessl

Join this live coding session to explore how developers can leverage AI tools, like Copilot, Claude 3.5 Projects, Cursor, and more. We will explore what it looks like to build applications today and how this could change in the future. As we build our demo application we'll discuss how these tools can be integrated into your daily routines and consider the axes of change and trust influencing their adoption within your workflow. This session is designed for developers looking to understand and adopt AI's growing role in software development.

About Simon: Simon Maple is the Founding Developer Advocate at Tessl, previously the Field CTO, and VP Developer Relations at Snyk, ZeroTurnaround, and IBM. He has been a Java Champion since 2014, JavaOne Rockstar speaker in 2014 and 2017, Duke’s Choice award winner, Virtual JUG founder and organiser, and London Java Community co-leader. He is an experienced speaker, having presented at most large conferences in the Java and Security spaces

Session 2: Neo4j for Java Devs: What's in the box? - Gerrit Meier\, Neo4j

Getting started with Neo4j instance and the Java ecosystem has never been easier than it is today. Be it Spring, Quarkus, or just the plain Java driver, get the latest update on our provided tooling and support in the Java ecosystem. Manually writing database statements in plain Strings can be error-prone, that's why we created our CypherDSL. We will have a ride from a basic driver example to a full object mapping supported enterprise application. Also, we will discuss on how to maintain, develop and evolve your database by using Neo4j-Migrations.

Let's also explore this option by taking advantage of already existing frameworks to stay focussed on your business code. In the end, you can decide for yourself what abstraction and use-case is right for you to get started with a graph database.

About Gerrit: As a software engineer at Neo4j Gerrit is developing the Neo4j Object Graph Mapper (OGM) and the Spring Data Neo4j (SDN). He is always looking for new things to learn and tries to make the world better by sharing knowledge and ideas. Not only because of that he is a leader of his local Java User Group.

Guess what? Data science and AI initiatives are still failing here in 2024—despite widespread awareness. Is that news? Candidly, you’ll hear me share with Evan Shellshear—author of the new book Why Data Science Projects Fail: The Harsh Realities of Implementing AI and Analytics—about how much I actually didn’t want to talk about this story originally on my podcast—because it’s not news! However, what is news is what the data says behind Evan’s findings—and guess what? It’s not the technology.

In our chat, Evan shares why he wanted to leverage a human approach to understand the root cause of multiple organizations’ failures and how this approach highlighted the disconnect between data scientists and decision-makers. He explains the human factors at play, such as poor problem surfacing and organizational culture challenges—and how these human-centered design skills are rarely taught or offered to data scientists. The conversation delves into why these failures are more prevalent in data science compared to other fields, attributing it to the complexity and scale of data-related problems. We also discuss how analytically mature companies can mitigate these issues through strategic approaches and stakeholder buy-in. Join us as we delve into these critical insights for improving data science project outcomes.

Highlights/ Skip to:

(4:45) Why are data science projects still failing? (9:17) Why is the disconnect between data scientists and decision-makers so pronounced relative to, say, engineering?  (13:08) Why are data scientists not getting enough training for real-world problems? (16:18) What the data says about failure rates for  mature data teams vs. immature data teams (19:39) How to change people’s opinions so they value data more (25:16) What happens at the stage where the beneficiaries of data don’t actually see the benefits? (31:09) What are the skills needed to prevent a repeating pattern of creating data products that customers ignore?? (37:10) Where do more mature organizations find non-technical help to complement their data science and AI teams?  (41:44) Are executives and directors aware of the skills needed to level up their data science and AI  teams?

Quotes from Today’s Episode “People know this stuff. It’s not news anymore. And so, the reason why we needed this was really to dig in. And exactly like you did, like, keeping that list of articles is brilliant, and knowing what’s causing the failures and what’s leading to these issues still arising is really important. But at some point, we need to approach this in a scientific fashion, and we need to unpack this, and we need to really delve into the details beyond just the headlines and the articles themselves. And start collating and analyzing this to properly figure out what’s going wrong, and what do we need to do about it to fix it once and for all so you can stop your endless collection, and the AI Incident Database that now has over 3500 entries. It can hang its hat and say, ‘I’ve done my job. It’s time to move on. We’re not failing as we used to.’” - Evan Shellshear (3:01) "What we did is we took a number of different studies, and we split companies into what we saw as being analytically mature—and this is a common, well-known thing; there are many maturity frameworks exist across data, across AI, across all different areas—and what we call analytically immature, so those companies that probably aren’t there yet. And what we wanted to draw a distinction is okay, we say 80% of projects fail, or whatever the exact number is, but for who? And for what stage and for what capability? And so, what we then went and did is we were able to take our data and look at which failures are common for analytically immature organizations, and which failures are common for analytically mature organizations, and then we’re able to understand, okay, in the market, how many organizations do we think are analytically mature versus analytically immature, and then we were able to take that 80% failure rate and establish it. For analytically mature companies, the failure rate is probably more like 40%. For analytically immature companies, it’s over 90%, right? And so, you’re exactly right: organizations can do something about it, and they can build capabilities in to mitigate this. So definitely, it can be reduced. Definitely, it can be brought down. You might say, 40% is still too high, but it proves that by bringing in these procedures, you’re completely correct, that it can be reduced.” - Evan Shellshear (14:28) "What happens with the data science person, however, is typically they’re seen as a cost center—typically, not always; nowadays, that dialog is changing—and what they need to do is find partners across the other parts of the business. So, they’re going to go into the supply chain team, they’ll go into the merchandising team, they’ll go into the banking team, they’ll go into the other teams, and they’re going to find their supporters and winners there, and they’re going to probably build out from there. So, the first step would likely be, if you’re a big enough organization that you’re not having that strategy the executive level is to find your friends—and there will be some of the organization who support this data strategy—and get some wins for them.” - Evan Shellshear (24:38) “It’s not like there’s this box you put one in the other in. Because, like success and failure, there’s a continuum. And companies as they move along that continuum, just like you said, this year, we failed on the lack of executive buy-in, so let’s fix that problem. Next year, we fail on not having the right resources, so we fix that problem. And you move along that continuum, and you build it up. And at some point as you’re going on, that failure rate is dropping, and you’re getting towards that end of the scale where you’ve got those really capable companies that live, eat, and breathe data science and analytics, and so have to have these to be able to survive, otherwise a simple company evolution would have wiped them out, and they wouldn’t exist if they didn’t have that capability, if that’s their core thing.” - Evan Shellshear (18:56)

“Nothing else could be correct, right? This subjective intuition and all this stuff, it’s never going to be as good as the data. And so, what happens is, is you, often as a data scientist—and I’ve been subjected to this myself—come in with this arrogance, this kind of data-driven arrogance, right? And it’s not a good thing. It puts up barriers, it creates issues, it separates you from the people.” - Evan Shellshear (27:38) "Knowing that you’re going to have to go on that journey from day one, you can’t jump from level zero to level five. That’s what all these data maturity models are about, right? You can’t jump from level zero data maturity to level five overnight. You really need to take those steps and build it up.” - Evan Shellshear (45:21) "What we’re talking about, it’s not new. It’s just old wine in a new skin, and we’re just presenting it for the data science age." - Evan Shellshear (48:15)

Links Why Data Science Projects Fail: The Harsh Realities of Implementing AI and Analytics, without the Hype: https://www.routledge.com/Why-Data-Science-Projects-Fail-the-Harsh-Realities-of-Implementing-AI-and-Analytics-without-the-Hype/Gray-Shellshear/p/book/9781032660301  LinkedIn: https://www.linkedin.com/in/eshellshear/  Get the Book: Get 20% off at Routledge.com w/ code dspf20   Get it at Amazon

Why do we still teach people to calculate? (People I Mostly Admire podcast)

Team '24 is about teamwork innovation—advancing the way people work together with deep human insight and breakthrough technology. Join the Berlin -Brandenburg ACE chapter as we highlight exciting updates from the annual flagship event and learn real-world best practices powering up teams across the world.

Agenda

4:00 PM: Registration

4:30 PM: Opening & Welcome

Recap Team'24 Phill Fox from Adaptavist

4:50 PM: Atlassian

Bartosz Robakowski

5:20 PM: Atlassian Data Center Security: Storing sensitive configuration data centralized in vaults

Presenter : Liza Kürzinger -scandio

Keeping your security standards even higher on containerized DC instances by storing passwords / tokens within vaults. And also maintaining it within clustered infrastructure with optimized security.

Don't forget Data Center

As most of the smaller Jira and Confluence instances have been moved to the cloud, we are tempted to lose sight of the Data Center instances: One reason for On Premise is often security - but what about the security of the Jira / Confluence configuration?

Data Center instances are often kept in Docker containers with Docker Compose

There are simple solutions to set up these environments - but they are not centralized

How can they be centralized?

One step further: In clustered infrastructures, such as kubernetes cluster, you use the complete security performance that is implemented in k8s and cloud solutions

5:35 PM: Standups refined: agile collaboration with NASA

Presenter: Björn Döhler - re:solution Have fun with an interactive session on the Scalable Standup Model with NASA - Not Another Standup App.

Transforming Scrum standups can have surprising ripple effects: engaging sessions motivate team members to be mutually supportive and practice active listening, ultimately increasing team velocity and the frequency of high-quality releases.

5:50 PM: Protect – Detect – Respond: Next level Cloud Security and Governance

Presenter: Sascha Wiswedel - Atlassian A work system like the Atlassian Cloud covers a variety of business cases, connects dozens, sometimes hundreds of teams in and across organizations. With all that intellectual property spread across this system of work, how do you make sure you can establish organization-wide clear policies and procedures and ensure compliance with regulations and standards?

Protect – Detect – Respond: Learn how to effectively combine Identity- and Access Management with Data Leakage Protection, built-in Threat Detection and outright remediation capabilities into an enterprise grade reliable zero-trust suite for your work system.

6:05 PM: JSM for HR: Your way to ESM

Presenter: Alex Post -Adaptavist Your way to ESM Discover how Jira Service Management can revolutionize HR operations towards comprehensive Enterprise Service Management. The session explores JSM’s transformative potential in HR.

Key Takeaways:

Ways to leverage the usage of JSM through the HR ESM case Strategic benefits: faster services and better collaboration

6:35 PM: Break

6:40 PM: "Empowering Decision-Making with Atlassian Analytics: Visualize and Conquer"

Presenter: Ansar Rezaei, Valiantys

Discover how Atlassian Analytics enhances your ability to visualize data from various Atlassian products and beyond. This session will explore out-of-the-box templates tailored for project management, service management, asset management, and DevOps, alongside the flexibility of custom SQL queries. Explore real-world scenarios using sample data from Atlassian One, demonstrating how teams can leverage this powerful tool for comprehensive insights.

7:00 PM: Gain More Valuable Insights From Your Jira Projects

Presenter: Julia Atlygina, Tempo

Description: Unlocking valuable project insights is crucial for meeting deadlines and staying within budget. Fortunately, with the right tools, any team can achieve this. Tempo’s flexible, modular portfolio management solutions for Jira – including Structure PPM, Gantt Charts for Structure PPM, Capacity Planner, Custom Charts, and Timesheets – empower organizations to surface key insights from multiple projects and teams, communicate real-time progress, detect risks early, and make the right decisions.

7:10 PM: From Zero To Hero: Data Governance Can Be Easy

Presenter: Hubert Kut - Appforge.ai

Discover the simplicity behind mastering JIRA configurations with our cutting-edge tool, Doctor for JIRA. Learn how to perform comprehensive health checks, automate cleaning, and improve data quality effortlessly. This presentation will guide you through implementing effective data governance practices that enhance system performance and user satisfaction, making JIRA management straightforward and efficient. Transform your approach to JIRA with actionable insights that lead to a cleaner, more organized, and productive environment.

7:30 PM: tbd

Presenter: Przemyslaw Wesolka - JAP Connect

8:00 PM: Break

8:10 PM: Enabling Teams with Atlassian Intelligence

Presenter: Ahmet Kilic - catworkx

8:40 PM: Closing

Presenter: ACE-BB / Phill Fox Recap of the event

8:50 PM: Social Networking

Network, grab drinks, and have fun

Speakers

Phill Fox - Adaptavist (Principal Customer Success Advocate)

Phill is a Principal Customer Success Advocate at Adaptavist, helping Atlassian users around the world make the most of their installations. He has worked with Atlassian tools for over 10 years and regularly contributes to the Atlassian Community. Working from Suffolk, he frequently works closely with Atlassian on new initiatives and solutions.

Sascha Wiswedel - Atlassian (Senior Solutions Engineer)

As Senior Solutions Engineer at Atlassian, Sascha Wiswedel helps Enterprise customers on their journey to the Cloud and the adoption of efficient ways of working. He has a focus on Security, Compliance and Data Protection.

Hubert Kut - Appforge.ai (Atlassian Solution Architect)

My journey in IT world started as a support engineer. I realised quickly that a lot of teams straggle with collaboration, transparency and tracking work done/undone. That was the time when Atlassian kicked the door down and jumpstarted my career. From that time I'm Atlassian Evangelist, Solution Architect and passionate about Agile Methodology. My business goal is to facilitate people's work b…

Alexander Post - veniture GmbH (Senior Solution Engineer & co-Founder veniture)

Alex is the co-founder at veniture and a passionate Atlassian expert with outstanding knowledge of the Atlassian tool suite. He mainly advises our enterprise customers from various industries and manages large scale Atlassian projects. www.veniture.net

Björn Döhler - re:solution (CEO)

Experienced Chief Executive Officer with a demonstrated history of working in the information technology andservices industry. Skilled in Professional Services, Data Center, Management, Pre-sales, and Telecommunications.

Liza Kürzinger - Scandio

M.A. studied German linguistics and computational linguist at the University of Augsburg and L.M.U. Munich. After working in IT operations and application management for major business applications for over 15 years, she is part of Scandio since 2021.

Bartosz Robakowski - Atlassian (Software Engineer)

Beginning his journey with Atlassian as an intern, he was promoted to software engineer within two years and is currently focused on enhancing the scalability and performance of the Forge platform. He happily works from home with his two feline companions, whom teammates consider partial contributors to his successes.

Ahmet Kilic - catworkx (Senior Atlassian Expert)

Als erfahrener Enterprise Agility Consultant, SAFe Fellow und leidenschaftlicher Atlassian Enthusiast setze ich mich dafür ein, Teams durch die transformative Kraft der Atlassian-Intelligenz zu befähigen. Mit einem tiefgreifenden Verständnis für die komplexen Anforderungen moderner Unternehmen unterstütze ich Teams dabei, fundierte Entscheidungen zu treffen und nachhaltige Verbesserungen in Pr…

Przemyslaw Wesolka - JAP CONNECT (Solution Engineer, Atlassian Consultant, Founder JAP CONNECT)

Als erfahrener Speaker biete ich inspirierende und praxisnahe Vorträge sowie interaktive Workshops an, die sich mit verschiedenen Aspekten von Jira befassen. Von der Einführung in die Grundlagen bis hin zu fortgeschrittenen Themen wie Automatisierung, Skalierung und Best Practices - meine Veranstaltungen sind darauf ausgerichtet, Wissen zu vermitteln, Fragen zu beantworten und einen Mehrwert f…

Julia Atlygina - Tempo (Senior Product Manager)

Julia is the Structure and Structure.Gantt Product Manager, and the inventive mind behind the Structure.Testy add-on. Julia is a diligent student of software development methodology, with more than a decade of professional experience. As a certified SAFe consultant, she started the SAFe initiative at the Structure product line. In her off hours she organizes and presents at local software even…

Ansar Rezaei - Valiantys

Ansar serves as an Atlassian Operations Engineer at Valiantys, specializing in Atlassian Cloud and Data Center products with a keen focus on improving infrastructure and developing custom integrations. Previously, as an Atlassian Consultant, Ansar managed significant cloud migrations and optimized project and service management processes for a diverse client base.

Moderators

Hubert Kut - Appforge.ai (Atlassian Solution Architect)

My journey in IT world started as a support engineer. I realised quickly that a lot of teams straggle with collaboration, transparency and tracking work done/undone. That was the time when Atlassian kicked the door down and jumpstarted my career. From that time I'm Atlassian Evangelist, Solution Architect and passionate about Agile Methodology. My business goal is to facilitate people's work b…

Astrid Sieben - veniture GmbH (Senior Sales Executive)

Kathryn Vargas - Tempo (Product Manager)

Hosted By

Hubert Kut, Atlassian Solution Architect

My journey in IT world started as a support engineer. I realised quickly that a lot of teams straggle with collaboration, transparency and tracking work done/undone. That was the time when Atlassian kicked the door down and jumpstarted my career. From that time I'm Atlassian Evangelist, Solution Architect and passionate about Agile Methodology. My business goal is to facilitate people's work by giving them tools that are available from anywhere and at anytime. Key words for myself are simplification and automation.

Astrid Sieben, Sales Manager

Kathryn Vargas, Product Manager

Global Partner

Atlassian (http://atlassian.com) Millions of users globally rely on Atlassian products every day for improving software development, project management, collaboration, and code quality.

Partners

re:solution (http://www.resolution.de/)

Valiantys GmbH (https://www.valiantys.com)

VenITure (https://www.veniture.net)

Adaptavist (https://www.adaptavist.com)

Scandio GmbH (https://www.scandio.de/en/) Scandio GmbH is a software company full of technology-enthusiastic consultants and developers. We have been Atlassian Platinum Solution Partners in Munich, Augsburg and Berlin for over 11 years and are active as an agile IT consulting and software development company.

Catworkx (https://www.catwork.com)

Tempo (https://tempo.io)

For over a decade, Atlassian customers have come together to network, share ideas, solve problems, and find new ways to use Atlassian products. Today, more than 15,000 people take part in Atlassian community events in more than 30 countries.

Complete your event RSVP here: https://ace.atlassian.com/events/details/atlassian-berlin-brandenburg-presents-clc-the-best-of-team24-may-24th-team-24-berlin-brandenburg/.

🎁 For most companies, B2B product innovation is a black box, resulting in frustrated Product teams and millions of dollars wasted on products that nobody needs. But it doesn’t have to be like this. Learn The B2B Innovator’s Map, an actionable, step-by-step, B2B-focused process to lead your team out of uncertainty, win the trust and support of your Leadership team, and increase your product’s chances of success.

⏰ Schedule (CET) 17:00 - 17:05 Intros 17:05 - 18:00 Presentation and Q&A

✨ About the Organizers We're on a mission to help companies discover and deliver great products faster. We do this by empowering our community to share knowledge generously. Through our consulting engagements, we do all the hands-on and unglamorous work of a Product Manager on an Interim basis (3-12 months). We onboard fast, align teams, and deliver outcomes. 😉

👋 Join the conversation on Telegram. 📲 Connect with us on LinkedIn. 📺 Watch our previous talks here. 🚀 Would you like to attend one of our in-person events? Check the calendar here. 💌 Don't forget to subscribe to our newsletter to be up-to-date with the latest Product News. 🤩 Step Up: Do you want to speak at our next events? Email [email protected]

This is a 90 minute, hands-on, step-by-step, guided tutorial of visual calculations. We'll write typical calculations such as running sum, moving average, change on previous, percentage of total and year-to-date.

We start with a Power BI file that has already has some data, in a standard star schema data model, so we can spend all our time building visuals and writing visual calcs.

Before the session, to ensure that we get to a good start, please prepare by installing Power BI Desktop if you don't have it already, downloading the sample Power BI file, and switching on visual calcs in preview. Here are the details.

1. Check that you have a recent version of Power BI Desktop, preferably the current April 2024 version, but at least the February 2024 version (when visual calcs were introduced). If not download from here.
2. Download the Power BI Desktop sample file that we will use in the tutorial from here. Check that you can open it in Power BI Desktop.
3. Switch on visual calcs in the preview features. In the menu\, choose File \| Options and Settings \| Options features\, then in the Options dialog\, choose Preview features\, check the "Visual calculations" box\, click OK and restart Power BI Desktop for the setting to take effect.

As this is a hands-on lab exercise, you will need to be able to share your screen, so we can help if something goes wrong. As a practical tip it is best to have two screens; one to watch the demo and another so you can repeat the steps on your PC.

Mark Wilcock will be leading the tutorial. Mark is the organiser of this meetup group (LBAG), and a Microsoft MVP. He runs a company providing courses in data and AI and of course Power BI - details of all courses here.

This is an online (Teams) meeting.

Please join Charlottesville Data Science for the talk Testing and Evaluating AI-powered Capabilities in Mission-Critical Situations from May Casterline! We'll be gathering in person at the Castle Hill Gaming office by the Shops at Stonefield.

How to find us

Castle Hill Gaming is located at 2065 Inglewood Drive, next to the Hyatt at the Shops at Stonefield. We occupy the ground floor of a building about 10 feet above street level; there are three floors of apartments above us, but don't be confused! Parking is located along Inglewood Drive and behind the Hyatt; walk to the closest corner and down the sidewalk to the Castle Hill front door in the middle of the block. "2065" will be above the door.

About the talk

Many organizations are exploring integrating modern AI capabilities throughout their systems and operations. Organizations that work in high-stakes or "mission-critical" situations, however, face distinctive challenges when adopting AI technology. These organizations often have robust, long-standing policies and practices governing how to test and evaluate new tools and technologies to ensure they are reliable, safe, and effective. Given the "black box", self-learning, adaptive, and data-centric nature of AI, existing testing and evaluation procedures usually do not fully translate to new, AI-enabled systems. To build sufficient levels of confidence to deploy AI capabilities in mission-critical scenarios, organizations need to develop and promote new, AI-specific test and evaluation procedures.

May Casterline recently co-chaired a study for the National Academies of Sciences, Engineering, and Medicine exploring the adoption of AI-enabled systems at the US Department of the Air Force. In this talk, May will share and summarize her committee's findings and recommendations about how the Air Force can develop and field AI effectively, safely, and responsibly. Many of these insights may apply more broadly across any organization trying to harness the potential of AI in high-stakes, mission-critical situations.

Description: Traveling Light, Fast, and Easy: DAX Without Time Intelligence Functions Have you ever had any of these problems with DAX Time Intelligence (TI) functions?

🔸 Overlapping, confusing functions that don't produce the results you expect them to? 🔸 Calculations that break when you move to weekly granularity, fiscal periods, or custom periods (e.g., semesters, trimesters, etc.) 🔸 Inability to work with custom calendars such as 4-4-5 🔸 Measure code that is difficult to understand and/or debug 🔸 TI measures that work, but perform slowly

There is a MUCH better way. I haven't used ANY of the DAX time intelligence functions in over two years, yet my DAX using this approach is simple and intuitive, straightforward to debug, runs fast, and easily handles any fiscal/custom/nonstandard time period or calendar.

I will walk through exactly how this approach works, and I expect by the end of the presentation you will have sufficient info and understanding to implement it in your reports as well.

You might be thinking - "this sounds great, but my organization requires that I use their date table which can't be modified". No problem - I will demonstrate how this approach can be implemented using either a custom date table OR entirely within measures.

So, what do you have to lose? (other than three dozen janky black-box functions...)

At the end of the Meetup we'll have a Raffle with prizes offered by Enterprise DNA: 1 year FREE Membership Licenses on EDNA Platform for one lucky winner from the Live attendees

Speaker: Brian has worked across the data field for 34 years in a mix of hands-on analytic roles and executive/senior management positions.

Since 2019, he has focused primarily on Power BI, first as the PM on his agency's first large-scale implementation of Power BI, and then as the Enterprise DNA Chief Content Officer.

He is now focused on writing about data - working on co-authoring his first book, blog, and nightly posts on LinkedIn, with nearly 9 million annual views.

Brian lives in Washington, DC with his wife Sue and two charming, but ill-behaved cats who often can be seen and heard interrupting his Power BI videos.

Blog: Coming soon! Socials : LinkedIn: https://www.linkedin.com/in/brianjuliusdc YouTube: https://www.youtube.com/@EnterpriseDNA

Join us for a meetup on September 14th at 18.30 at Zalando, Berlin!

18:30: Join us for a drink

Please have your full and real name in your profile description because the security team will check if you're registered when you arrive.

18:45: Rankquest: Benchmarking Search API Ranking with Elasticsearch (Jilles van Gurp)

Search Ranking is something that many companies that use Elasticsearch struggle with.

Something we noticed while helping various clients is that many companies never evolve to having a systematic approach for testing their search ranking quality. It's too abstract for them; they don't know where to start with this, and they don't really get how this should be done or even why this is important.

In this presentation we present and unveil our new ranking tool, Rankquest Studio, which aims to address some of these issues. Rankquest emerged out of our frustration with existing tools and approaches in this space and we'll reflect a bit on the requirements we have for this before diving into a demo. Rankquest Studio, is open source, web-based, easy to use, and it can be used to to build out test benchmarks for evaulating your search solutions.

19:15: Generative Black-Box Testing for Evaluating Search Quality (Oliver Trosien @ Zalando)

We present a novel generative Black-Box Testing approach that uses semantically equivalent queries (e.g. “rote Kleider”, “Kleid rot”) for introspecting the quality of a search engine. At Zalando, we developed a tool for finding search quality problems at scale with the help of mass-generating semantically equivalent query variants. This is a novel way to find relevance problems that complements other approaches that use customer metrics or ground truth data. The tool allows easy extension with new test scenarios and languages by non-technical native language speakers. We will show how it was used to continuously monitor search relevance, to find regressions, and how you can implement such a tool yourself. Here are some questions that we’ll discuss in the session:

• How can you track search quality at scale?
• What kind of quality issues does generative black-box testing (not) uncover?
• Can it be used to test semantic or vector search quality?

20.00: Pizza

Special thanks to our hosts, Zalando!