At King, we ingest and analyze around a trillion of game events weekly to enhance gameplay and deliver player experiences to over two hundred million monthly active users. These game events enable us to improve our games via game observability, features delivery, and machine learning.

However, collection and storage of these game events bring a variety of challenges due to its sheer volume and high speed.

To address the challenges, we have developed an in-house product called Kingestor, which allows us to ingest our game events in an effective manner. It processes game events in near real-time with just a ten-minute latency, loading approximately five million events per second. Kingestor ensures data integrity through event reconciliation and deduplication, providing accurate, real-time insights for both business and technical applications. It is a scalable and adaptable product, which is designed for use across the gaming industry, making it easy to implement for businesses handling large-scale data.

Methods to manage data and its authorized usage in Google Cloud Platform, including access control through data governance and enforced security measures:

• Fine grained access control in BigQuery by enforcing IAM policies
• Column level access control by defining taxonomies and policy tags
• Best practices for using taxonomies and policy tags
• Row level access control in BigQuery - Access control restriction by using resource tags.

Best practices like naming conventions, proper data asset descriptions, or ownership tagging, are a must have to ensure proper governance across your data landscape. Yet still they often require manual effort and a trained, knowledgeable user base to put in place, unfortunately leading to those practices not being followed in the majority of cases. The only way to ensure those rules are followed to the letter, is by baking them in as defaults into the user experience of your data platform. HelloFresh is the world's leading meal kit company and global integrated food solutions group, shipping over a billion meals to customers per year. This talk will present HelloFresh's in-house, low-code, config-driven data engineering framework that was constructed to offer data governance and best practices out of the box. You will learn about the architecture around its open source components, and get a demonstration of the user experience designed to enable even less technical data practitioners.

Our presentation will cover the development and adoption of a private AI chat platform within our company. We will discuss the technical architecture, including the integration of multiple large language models (LLMs) and chat engines from various providers. A significant part of the presentation will focus on our user adoption strategy, which includes conducting workshops and identifying internal advocates to promote the technology. We will also highlight how we use this platform as a unified interface for testing new AI experiments, which streamlines development and user feedback. Additionally, we will share our experience in creating separate Python packages for chat engines to ensure scalability and reusability. The aim is to provide a comprehensive view of both the technical and human aspects of implementing AI solutions in a corporate environment.

What makes a great meme? Is it the template? The reference to recent events? Or perhaps sheer luck? Using the image embedding pipeline with the refined Vision Transformer model by Google, we explore the memesphere (yes, it's a word) of Reddit, and it's most popular meme subreddit: r/memes. We brew a recipe for the best memes, by analyzing the upvotes and comments statistics. We determine the most similar memes in terms of content and graphics to establish relations and form clusters segregated by meme templates. Finally, we answer the world-shaking question: What was the best meme of last year?

The future of Artificial Intelligence in software testing: its transformative impact within the telecommunications industry. As AI continues to evolve, organizations are leveraging intelligent solutions to optimize testing processes, enhance product quality, and reduce time-to-market. Drawing from real-world use cases from Ericsson AB, this presentation will dive into how AI is revolutionizing testing methodologies, addressing challenges in AI deployment, and setting the stage for the next leap in testing innovation. Attendees will gain actionable insights into integrating AI into testing pipelines, handling the complexities of large-scale deployments, and overcoming the challenges that come with AI adoption in the software testing space.

We want to showcase that successful implementation of AI solutions (open-weights semantic search for customer care) doesn’t have to be costly and can deliver solid ROI - contrary to the growing sentiment in the media as we seem to be entering the trough of disillusionment (as per Gartner’s hype cycle terminology). In our internal tech talks, our peers were particularly interested in some of the thinking process behind certain decisions that we made. For instance they wanted to know how we evaluated the most suitable model from the MTEB leaderboard, how we organized the embeddings to fit our knowledge base, why we chose this and not a different vector store and more. Interestingly, this implementation and heavy internal promotion among non-tech folks spurred an avalanche of ideas from other departments. We want to spread the experience and believe there’s no better place to do this than at the tech-heavy BigData Technology Warsaw Summit.

As organizations increasingly rely on data to drive decision-making, the ability to quickly access and analyze data has become essential. However, SQL querying remains a technical hurdle for many, slowing down workflows and limiting data access to specialized team members. This session will demonstrate how LLM-based Text-to-SQL tools can close this gap, enabling non-technical team members—such as product managers and business analysts—to generate SQL queries using natural language. In this talk will explore how to implement Text-to-SQL solutions for databases, discuss strategies to improve query accuracy through prompt engineering techniques, and optimize results by incorporating database structure representations. Attendees will walk away with actionable insights on how to empower their teams, streamline query development, and increase overall productivity.

Recommendation systems are an essential part of most e-commerce industries, often responsible for a significant portion of revenue. However, every branch of this industry has its own set of exceptions and challenges that affect how recommender systems have to be designed. In airlines, these exceptions become extreme as returning visitors become sparse, many purchases are anonymous, and items, such as flight tickets, can be sold at different prices depending on the circumstances. To overcome these challenges, we propose a simple method that utilizes information collected about users and items, omitting the need for extracting user/item embeddings with matrix factorization. Additionally, we will talk about how we used a Feature Store as a foundation for this project and why it could be beneficial to implement it in your Data Science team as well.

Exploration how our system uses a graph-based approach to store transactions and enhance fraud controls with advanced features, boosting the effectiveness of both ML models and static rules. Presentation of key components of the system, including a real-time feature computation service optimized for low latency, a visualization tool for network analysis, and a mechanism for historical feature reconstruction.

The evolution of AI has seen a remarkable transition from standalone language models (LLMs) to compound systems integrating diverse functionalities, culminating in the rise of agentic AI. This talk traces the journey of AI systems, exploring how agentic AI enables autonomous reasoning, planning, and action, making it a pivotal development in solving complex, dynamic problems.
We will dive into the principles of agentic AI, discussing how it works and why it is essential for creating adaptive, task-oriented solutions. The session will then introduce **CrewAI**, an open-source Python package that simplifies the development of intelligent agents. Through a practical use case, participants will learn how to implement their first agent with CrewAI, gaining hands-on insights into leveraging this powerful tool to unlock new possibilities in AI-driven applications.

Fast Deliveries, a freight forwarding company, faced a challenge when several of their forwarders violated non-competition agreements by working for their competitor, WHILE (names anonymized), and transferring clients during this period. The investigation centered on analyzing data from Trans.eu - a major European logistics platform where freight forwarders post and accept transportation orders, essentially serving as a digital marketplace crucial for day-to-day logistics operations.

Our team was tasked with developing a methodology to identify these fake accounts and connect them to specific former employees. Combining network analysis, geographic patterns, and temporal data allowed us to identify suspicious accounts with high confidence.

Attendees will learn about the specific investigation and gain insights into analytical techniques they can apply to their own data challenges. We will demonstrate our methodology using anonymized data from the actual investigation.

Dive into the practical and strategic considerations when choosing between these two approaches for creating effective AI agents. Prompt engineering has risen as a fast, adaptable, and low-cost way to harness the capabilities of LLMs. However, its performance often correlates directly with the size of the model - larger, more costly models are required to achieve the desired results. This trade-off raises questions about scalability and cost-efficiency, especially for organisations with resource constraints.

On the other hand, fine-tuning offers a path to tailor models for domain-specific tasks or nuanced interactions, delivering consistent performance even with smaller models. While it demands more resources upfront, fine-tuned solutions can lead to significant long-term savings by reducing reliance on oversized models:

• The strengths and limitations of prompt engineering vs. fine-tuning in AI agent development
• Cost implications: why prompt engineering often requires larger, more expensive models to perform well
• Fine-tuning as a solution to achieve domain-specific precision with smaller models
• Case study: the TUI AI travel assistant for the UK market and lessons learned
• A hybrid approach: combining prompt engineering and fine-tuning for best results.

The purpose of presentation is to demonstrate possibilities of Generative AI in the context of business intelligence. I'll focus on Copilot, AI Skills in MS Fabric, and AI/BI Genie in Databricks. The presentation will contain description of each tools, comparison, and the demo at the end. I'll present how to prepare data model and data to make it available for Gen AI. I'll demonstrate how these tools can support data democratization in an organization.

I'm also considering to include Conversation Analytics from GCP