What’s inside the playbook? This isn’t just theory. It’s a tactical guide to the 7 frameworks that are defining the 2026 data landscape: The kappa shift: Learn why treating everything as a stream is the secret to 100% data consistency. ELT vs. ETL: Why the transform-last approach is saving engineers 20+ hours of maintenance a week. Modern data lakes: Practical…
In this tutorial, we demonstrate how to federate fine-tuning of a large language model using LoRA without ever centralizing private text data. We simulate multiple organizations as virtual clients and show how each client adapts a shared base model locally while exchanging only lightweight LoRA adapter parameters. By combining Flower’s federated learning simulation engine with…
Earth observation (EO) constellations capture huge volumes of high-resolution imagery every day, but most of it never reaches the ground in time for model training. Downlink bandwidth is the main bottleneck. Images can sit on orbit for days while ground models train on partial and delayed data. Microsoft Researchers introduced ‘OrbitalBrain’ framework as a different…
The gap between data-driven and data-lagging companies is defined by one thing: Pipeline architecture. If you are still battling fragmented batch cycles, data swamp silos, and brittle monolithic code, you aren’t just losing time – you’re losing your competitive edge. Our Data pipeline design playbook (2026 edition) is the definitive blueprint for modern data engineers….
In production, hallucinations don’t show up as errors: they show up as responses people initially trust. This initial trust can be costly, however. What we’re seeing across real deployments is that hallucinations aren’t a single bug to fix. They’re a system-level behavior that emerges when a few things go wrong together: They don’t originate in the…
Catch up on every session from Generative AI Summit Washington, D.C., with sessions from the likes of OpenAI, US Bank, Meibel and more.
Robots are entering their GPT-3 era. For years, researchers have tried to train robots using the same autoregressive (AR) models that power large language models (LLMs). If a model can predict the next word in a sentence, it should be able to predict the next move for a robotic arm. However, a technical wall has…
How Stability AI is helping shape Austin’s practical approach to generative AI 💡 For many teams, the challenge is no longer whether to use large AI models, but how to use them responsibly, efficiently, and at scale (preferably without surprising the finance team!) In Austin’s growing AI ecosystem, where startups and enterprises alike focus on…
In this tutorial, we show how we treat prompts as first-class, versioned artifacts and apply rigorous regression testing to large language model behavior using MLflow. We design an evaluation pipeline that logs prompt versions, prompt diffs, model outputs, and multiple quality metrics in a fully reproducible manner. By combining classical text metrics with semantic similarity…
State-sponsored hackers are exploiting AI to accelerate cyberattacks, with threat actors from Iran, North Korea, China, and Russia weaponising models like Google’s Gemini to craft sophisticated phishing campaigns and develop malware, according to a new report from Google’s Threat Intelligence Group (GTIG). The quarterly AI Threat Tracker report, released today, reveals how government-backed attackers have…