DSC DACH 25
2025-10-14..2025-10-16
Vienna, Austria
| title | author | length |
|---|---|---|
| Get the vibe? | Catalin Gheorghiu | 01h 31m |
Builds a working web photo editor across browser and desktop AI tools — tries Google AI Studio, Firebase Studio, tempo.ai and several VS Code forks (Cursor… | ||
| A Deep Dive into User-Centered Power BI Report Design | Paula Garcia Esteban | 01h 21m |
Claims a repeatable, design‑thinking process that shifts effort from styling to solving user problems, using mockups.com for interactive low‑fi prototypes, a… | ||
| Thinking in Graphs: The Cognitive Foundations of Agentic AI | Elaheh Momeni | 01h 26m |
Claims that Graph RAG plus the Model Context Protocol produce explainable, agentic AI, demonstrated with a Neo4j-backed knowledge graph feeding LLM context… | ||
| Azure AI Foundry Survival Guide | Andre Melancia | 01h 29m |
Azure AI Foundry combines generative models, vision, speech, document extraction and avatar/video tools into one portal; the model catalog lists about 11,000… | ||
| Build your MVP (Minimal Viable Product/Prototype) in 90 minutes | Kasia Sadowska | 01h 28m |
Claim: turn an idea into a working MVP in 90 minutes using no-code and AI. Workflow uses Uizard for fast UI, Lovable to generate pages, Superbase backend and… | ||
| Building Responsible AI Agents in Decentralized Open-Source Environments | K.Dasgupta | 01h 42m |
A minimal on‑prem pipeline is built and demoed end to end using LLaMA, Gemma, GPT‑OSS plus MCP, A2A, AutoGen and Google ADK with vLLM, Hugging Face… | ||
| A Practical Starter's Guide to building LLM based projects | Marcin S. | 01h 34m |
Hands‑on demo that builds from a single OpenAI API prompt to a working pipeline using LangChain, showing: programmatic calls to models (GPT family and smaller… | ||
| Why Visual Data Analysis with Tableau Matters More Than Ever in the AI Era | C. Mata | 01h 32m |
Demonstrates using Tableau Desktop and Tableau Agent/Plus to build bump charts, dual‑axis trends and annotated dashboards that validate KPIs against source… | ||
| Building a Semantic AI Layer in Snowflake: From Metrics to AI Insights | V. P., R. M. | 01h 13m |
Demo shows Snowflake running Cortex AI functions (complete, search, classify, summarize) over ingested CSVs to build a semantic YAML model, load data into… | ||
| Hacking the Blackbox | Brinnae Bent | 34m |
Argues that black‑box models are exploitable and those exploits can be turned into fixes: uses Adversarial Alignment to probe vision, sensor and LLM systems… | ||
| Augmented Intelligence — The Future of GenAI? | Christian Casari | 25m |
Argues Augmented Intelligence boosts human decisions with concrete systems not promises: BVAEB (1.5M insured) gets a hybrid search plus user feedback to cut… | ||
| From Data to Precision: Fine-Tuning LLMs for Proprietary Query Languages | M. Spanring | 31m |
Argues that fine-tuning plus a modern data foundation is required to turn natural language into exact, executable proprietary queries, not just LLM prompts… | ||
| Why Agents Don’t Ship: Closing the Production Gap for Real Business Impact | J.P. Panter | 30m |
Most agent demos never reach production because teams skip engineering the pipeline parts that actually make them reliable: the talk shows that building… | ||
| Symbolic Reasoning in telecom oriented LLMs | Ali Mokh | 28m |
Claims a hybrid approach that pairs LLMs with knowledge graphs, constraint solvers and formal logic to reduce hallucination and enable symbolic… | ||
| Where GenAI projects fail & how can leaders prevent it | Ruben H., Annalisa C., Diogo R. B. | 49m |
Claims that 80–85% fail rate for AI projects comes from mismatched goals and weak data not model limits, with pilots often built in the cloud for two to three… | ||
| Importance of data in the time of LLMs | Zrinka Puljiz | 43m |
Claim: LLMs let teams skip months of data collection by prompting or synthetic generation, but production needs benchmarks and cost controls. Example: YouTube… | ||
| Implementing Natural Language Analytics with Snowflake Cortex | Ved Prakash | 36m |
GitLab implements conversational analytics with Snowflake Cortex and a simple Streamlit front end, starting with a 3–5 table POC and evolving through prompt… | ||
| From Data to Precision: Fine-Tuning LLMs for Proprietary Query Languages | M. Spanring | 21m |
Company converts logged DQL queries (redacted) into a fine‑tuning dataset by reimputing plausible values and generating NL annotations with an LLM, clusters… | ||
| Intent-Driven Automation for Machine Data: Natural Language to Edge | Verena P., Ioan T. | 22m |
Claims a working pipeline that turns natural language into executable edge deployments using a chatbot, a knowledge graph (RDF/JSON‑LD, SHACL) and LLMs, with a… | ||
| Scaling AI: Turning Pilots into Production | Karl Kugler | 19m |
In his talk, Karl addressed the challenges companies face in moving beyond AI pilot projects, which often remain siloed. He shared practical insights on… | ||
| Thinking in AI: How to harness the mindset of innovation | Simone Scholz | 13m |
In her talk, Simone emphasized that the true power of AI lies in the mindset guiding its use, not just the tools themselves. She explored how forward-thinking… | ||
| AI-driven DataOps: Building data products with a taste of the future | Radovan Bacovic | 31m |
GitLab Data Team experiments four architectures: CSV cloud API prototyping, cloud connectors to Snowflake via MCP, Snowflake-first paths using Snowflake Cortex… | ||
| Next Gen ETL Generators | Miljenko Vukovic | 15m |
Argues that ELT generators remain practical for cloud warehouses by parsing an Excel spec or SQL to auto-produce dbt models, Matilion JSON jobs… | ||
| From Tools to Partners: The Rise of Agentic AI | Martin Flechl | 45m |
Google processes 1.3 quadrillion tokens/month and token costs fall from ~$30 per million to ~$0.08, driving explosive LLM use; model capability doubles… | ||
| Demystifying AI Hardware: NVIDIA Evolution and Costs | Markus Warg | 40m |
Overview of actual hardware, costs and tradeoffs: a DGX Spark desktop-equivalent costs ~€4,000 and runs solo development or privateGPT-style demos; RTX Pro… | ||
| AI that is gold does not glitter - using intelligence in Modern Infra | Josema Cavanillas | 26m |
Claims concrete results: a deployed Polaris agent performs predictive autoscaling, anomaly detection, root‑cause research and self‑healing; a four‑level kbert… | ||
| Federated Data Mesh: The Hidden Foundation for Trustworthy AI | Mohamed Mansour | 28m |
Claim: a federated Data Mesh built on Google Cloud (Databricks as logical layer, BigQuery + GCS for storage, Terraform and CI/CD for ops) keeps infrastructure… | ||
| AI Factory - Enabling Scalable Innovation & Intelligent Systems | T. Kitzler, T. Singh | 40m |
Claims that large‑scale training now requires liquid cooling and that inference workloads can often run on smaller air‑cooled systems; recommends containerized… | ||
| nothing matches | ||