DSC DACH 24
2024-09-11..2024-09-13
Vienna, Austria
as the organisers put it
Explore the structure of the DSC DACH 2026 program: keynotes, workshops, AI systems, enterprise sessions, and the themes shaping the DACH AI market.
who it's for
data engineers, machine learning engineers, data scientists, MLOps engineers, cloud architects, AI product managers, software engineers
topics
Snowpipe streaming ingestion; Snowflake storage integration; time series demand forecasting with XGBoost; lag and rolling-window feature engineering; model explainability with SHAP and LIME; Llama 3.1 RAG pipelines with LlamaIndex; OpenAI GPT-3.5-turbo embeddings with Pinecone; Azure Computer Vision with Azure OpenAI; WatsonX on Red Hat OpenShift; retrieval-augmented generation implementation; document intelligence invoice processing
| title | author | length |
|---|---|---|
| Snowpipe Fundamentals: Ingesting Streaming Data into Snowflake Made Easy | Ved Prakash | 01h 30m |
Step‑by‑step setup shows a working pipeline: create an S3 bucket, attach an IAM policy and role, create a Snowflake storage integration and external stage… | ||
| Predictability Check: Demand Forecasting | Hillary Farmer | 59m |
Step-by-step Python pipeline that engineers lag/shift features, rolling-window stats and holiday flags, trains XGBoost models and exports a simple Streamlit… | ||
| Navigating ML Explainability: From Theory to Implementation | Orsalia Andreou | 01h 29m |
Overview of practical explainability: regulatory hooks are the EU AI Act and GDPR, and explainability must be designed from project start through feature… | ||
| Build RAG using Llama 3.1 | Amir Siddiqui | 01h 12m |
Step‑by‑step builds a working RAG pipeline that uses Llama 3.1 8B instant via Grok Cloud API key, converts a sample Paul Graham essay into embeddings with… | ||
| Computer Vision in Azure | Pawel Ekk-Cierniakowski, Mariusz Wiecha | 01h 33m |
Three production demos show concrete outcomes: invoice processing uses Azure AI Document Intelligence plus a Python Azure Function pipeline that polls Gmail… | ||
| Generative AI for Businesses | Erik Ternav | 01h 20m |
Claim: generative AI will add trillions to the economy and 81% of enterprises plan to use foundational models, with 90% adoption in US/UK/Germany and 70% of… | ||
| Building a Simple RAG Application: From Fundamentals to Implementation | Stanko Kuveljic | 01h 28m |
Hands-on demo builds a Retrieval-Augmented Generation pipeline end-to-end using OpenAI GPT-3.5-turbo for generation and embeddings and Pinecone vector DB for… | ||
| nothing matches | ||