BigData Republic

When chatbots go off the rails... Chris Bakke convinced a Chevrolet chatbot to sell him a car for $1. (Yup, one dollar.) Amusing? Definitely. A wake-up call? Even more so. Building a chatbot has never been easier, but building safe ones? A whole different story. Take Air Canada. In 2024, a court ruled the airline liable for a chatbot’s poor refund advice. The judgment? “negligent misrepresentation.” Ouch. Whether it’s agreeing to absurd deals, making absurd promises or giving absurd faulty advice. The takeaway is simple: powerful chatbots need guardrails. So, how do we keep chatbots in check? Three methods come to mind: 1 / Train your own LLM Offers most control, but comes with an extreme price tag and a massive need for data. Impractical for all of us. 2 / Fine-tune an existing LLM You can customize a pre-trained model to suit your needs. But still pricey and data-heavy for most of us. 3 / Add guardrails The more practical, cost-effective solution that keeps the bot aligned with company policies without modifying the core model. For most situations, guardrails are the way to go. Want a good example? Check out Sam Sweere article on how he built a chatbot for the fictional car company, LLMotors. Without any "free car" fiascos. Not only telling, but practically showing how guardrails can be really useful. Worth the read, unless you’re into losing cars for free 😉

3 lessons from improving an energy forecasting system with Eneco We partnered with Eneco to improve an AI system that forecasts long-term energy demand. Sounds techy? It was. Beyond the technical stuff, here's what really made the difference for us: #1 Involve users from day one, often. Waiting to bring users in until the end? Big mistake. We included users from the very beginning. Regular chats about progress, early peeks at results, and walk-throughs of our design choices built an open dialogue. This approach led to greater trust and acceptance of the forecasting model. #2 Be open about what your model can (and can't) do. If you want trust, start with transparency. We were upfront about both the strengths and limitations of our model, which helped users understand exactly what they were working with. When users know the capabilities and boundaries of a tool, they're much more confident relying on its predictions. #3 start small, scale smart Proof of concept is everything. We started small, testing our model in a controlled environment. This gave us room to fix issues, refine accuracy, and prove the model's value before launching it across the organisation. Successful data-driven projects rely on more than tech, code, and algorithms. They need user involvement, open communication, and smart phased rollouts.

Curious to learn more? https://lnkd.in/eMp9_xY2

Did you know that we all practice data science every day? Think about it: When should I leave to avoid traffic? What’s the weather tomorrow? How much should I sell my second-hand bike for? For questions like these, we crave exact answers. Single numbers. Certainty. When it comes to data science and machine learning models, we tend to expect the same precision. Single-point predictions we can act on without hesitation. But here's the reality: some things in life are inherently uncertain, regardless of how much data we have. Even with perfect information, we can't predict everything with absolute certainty. And yet, we still tend to rely on one number. Imagine selling that bike online. A traditional model might tell you: “it's worth €200”. Seems straightforward, right? But what if the buyer cares more about the color? Or they’re a hardcore deal-hunter? And what about the seasons? Enter conformal prediction. Instead of one number, you get a range: “€180 to €$220 with 95% confidence”. Now, you have a smarter way to make decisions. You can balance between making a quick sale and maximizing your profit. Why care? → More realistic answers → Uncertainty becomes a tool rather than a limitation → Better risk assessment and planning Of course it’s not about bikes. Think about a doctor predicting a patient’s recovery time. “7 days” isn’t nearly as helpful as “5 to 9 days with 95% confidence.” It’s more precise, more trustworthy. So why settle for a single prediction? If you’re curious to learn more, check out the blog post by our machine learning engineer, Robbert van Kortenhof, where he dives deeper into Conformal Predictions. Including a practical Python code implementation! Link in comments 👇

Dima Baranetskyi shares some in-depth insights on message retention in Kafka. You might want to give this a careful read, especially when dealing with high standards for data privacy.

Hackathons with social impact. Sounds good? It was. A little while ago we hosted a 'hacky day' in collaboration with the Centre for Information Resilience (CIR). Two days of energy, creativity and a bit of chaos. Why did we do this? ↳ First and foremost: use our technical expertise for a good cause. ↳ But also: to support CIR in their fight against war crimes and disinformation. ↳ And: Let colleagues think beyond their client work. Our focus? CIR’s "Eyes on Russia" project. A project to collect and verify videos, photos, satellite imagery and other media related to Russia’s invasion of Ukraine. Our objective was to provide journalists, NGOs, policymakers and the public access to verified, trustworthy information. Their challenge: "We need a way to automatically tag drone footage." It saves analysts a lot of time and reduces exposure to graphic content. This allows them to gather more evidence and better represent victims of war crimes. Our solution: ↳ An MLOps pipeline built around an AI model that recognizes drone footage. ↳ Fully integrated with CIR’s cloud platform. ↳ Designed for simplicity, scalability, and maintainability. The result? A functional architecture, soon to be fully deployed into production. The vibe? Chaotic, yes. But the room was filled with passionate discussions, fresh ideas, and a drive to make a real-world impact. When you’re working on something with genuine social value, it’s amazing how much it fuels your motivation.

Why is real-time data analysis still so rare? It’s surprising, especially when companies like Airbnb, Stripe, Netflix and LinkedIn thrive on fast decisions powered by real-time data pipelines. Most companies? They’re stuck funneling data into traditional systems, missing the opportunity for true real-time insights. Real-time data gives you an edge—anticipating trends, reacting as things happen, and essentially operating in the “future.” Imagine that vital sectors of society process data in real-time. How many opportunities could this open for us? Here's how real-time data pipelines work: → 1 ) Data Ingestion: Data is constantly being generated—think clicks, app activity, IoT sensors. Tools like Apache Kafka ensure that data flows continuously, capturing it in real time, no delays. You get the data the moment it’s created. → 2 ) Stream Processing: Here’s where the real value starts. Data needs to be prepped and cleaned immediately. Tools like Apache Flink and Kafka Streams process data streams in real-time, ensuring that it’s ready for instant analysis. No batch processing. No waiting. → 3 ) Real-Time Storage & Analysis (This is where the magic happens) Apache Druid / This is your go-to for analyzing high-volume data at lightning speed. Think real-time dashboards tracking millions of events per second—user behavior, performance metrics, anything you need to see now. Apache Kylin / Excels at pre-aggregating massive datasets, which means it runs complex analytics before you need them. Result? You can generate detailed reports faster than you thought possible. Apache Pinot / Designed for sub-second query responses, perfect when speed is critical—like monitoring live marketing campaign performance or tracking product metrics in real time. You get the answers as fast as you can ask the questions. → 4 ) Visualization: Data is useless if it’s not actionable. That’s why you need tools that can present the information clearly, in real-time dashboards and reports. Whether you’re tracking KPIs or operational metrics, the data you see is always up to date. By integrating tools like Apache Druid, Kylin, and Pinot into your data pipeline, you’re enabling your business to act in real-time. No lag, no guesswork—just fast, informed decisions when they matter most. Want to see in detail how it’s done? In Part 1 of our blog series, we dive deep into how these technologies power real-time insights. Link in comments.

Big welcome to Frederik and Robbert! Frederik, our newest data engineer, loves taking apart complex problems and turning them into simple, elegant systems. He’s the guy who’s always thinking ahead, already solving tomorrow’s problems. He’s off to a strong start with a project at Eneco. Robbert? He’s here to breathe life into machines as a machine learning engineer. From sensor data to pricing data, he finds the patterns that matter. And somehow, he makes the hardest stuff sound simple. Already diving into a collaboration with Kickstart AI. These two bring fresh ideas and major skills. We're very happy to have them on board and we're looking forward to seeing what they build next!

Attending PyData Amsterdam and need a break? You could grab another brochure.. Or collect high scores🏆 You could endure more small talk.. Or challenge your friends😅 You could sit through another presentation.. Or wake up your brain🤓 The choice is yours! Come find us at our booth and tumble down the Prompting Rabbithole with our CTF challenge. You'll have to beat: 1. Pascal van Luit with a score of 4524 2. Fred Chair with a score of 3704 3. Jacopo Pierotti with a score of 3704 Time is ticking, see you soon!

Are you one of the few who can outsmart our AI? We’ve built a game, and it’s about to take over PyData Amsterdam. What's at stake: → Bragging rights. There'll also be a few pretty cool prices to win. Here's your mission, should you choose to accept it: 🔓 8 levels where AI evolves, adapts, and fights back. 🔑 Craft the perfect prompts to unlock passwords. ❤️ 3 lives to reach the top. Don’t just watch. Tag your smartest friend and challenge them. Think you’ve got what it takes? Go down the Prompting Rabbithole with us at PyData Amsterdam. #promptingchallenge #largelanguagemodels #llm #capturetheflag #ctf #pydataamsterdam