talk-data.com

Abstract: Detecting problems as they happen is essential in today’s fast-moving, data-driven world. In this talk, you’ll learn how to build a flexible, real-time anomaly detection pipeline using Apache Kafka and Apache Flink, backed by statistical and machine learning models. We’ll start by demystifying what anomaly really means - exploring the different types (point, contextual, and collective anomalies) and the difference between unintentional issues and intentional outliers like fraud or abuse. Then, we’ll look at how anomaly detection is solved in practice: from classical statistical models like ARIMA to deep learning models like LSTM. You’ll learn how ARIMA breaks time series into AutoRegressive, Integrated, and Moving Average components, no math degree required (just a Python library). We’ll also uncover why forgetting is a feature, not a bug, when it comes to LSTMs, and how these models learn to detect complex patterns over time. Throughout, we’ll show how Kafka handles high-throughput streaming data and how Flink enables low-latency, stateful processing to catch issues as they emerge. You’ll leave knowing not just how these systems work, but when to use each type of model depending on your data and goals. Whether you're monitoring system health, tracking IoT devices, or looking for fraud in transactions, this talk will give you the foundations and tools to detect the unexpected - before it becomes a problem.

In this talk, we're excited to show you how we built ScaleDown, a Chrome extension that makes your AI interactions more efficient and environmentally sustainable using prompt compression! As more people use AI tools such as ChatGPT, Claude, and Gemini instead of Google Search, few realize the massive carbon footprint each interaction generates. We will talk about our journey from recognizing this hidden environmental cost to creating a solution that is helping users reduce their AI-related emissions by up to 80%. Finally, we'll share how developers can contribute to our open-source Python package powering ScaleDown's prompt compression. Whether you're interested in improving our compression algorithms, enhancing our emissions calculation methodology, or expanding compatibility with additional AI models, we'll show you how you can get involved!