GenAI Learnathon: Enrich Data Analytics with GenAI - Live from Harvard, MA

Learn how to enrich data analytics with GenAI without writing a line of code.

During this event, we will explore a fraud detection use case and we will exploit the multi-language capabilities of GenAI to automatically generate custom alert messages.

During this hands-on workshop you will get familiar with the free low-code tool KNIME Analytics Platform, techniques for fraud detection, and the use of GenAI via the KNIME AI Extension (Labs).

Want to join the event but can’t attend onsite? We will stream the first part of the event online and share the exercises with our online audience for self-studying. Register here for the online event.

Completely new to KNIME? We’ve got you covered! We will start with an introduction to KNIME Analytics Platform, making it easy to follow along even for newbies!

Without any coding experience you will learn to:

• Detect frauds in investment contracts visually and using statistical techniques
• Prompt engineer an LLM via the OpenAI integration
• Create a vector store from a corpus of different types of investment contracts (the knowledge base)
• Build and deploy a data app to display email alerts and make it available to authenticated users on a web browser

Other KNIME AI integrations we will discuss include GPT4All, Hugging Face, Chroma, FAISS and more.
Please come prepared and bring your laptop with KNIME Analytics Platform and the KNIME AI Extension (Labs) already installed.

Please note if you want to join onsite, registration is mandatory.

Agenda:

6:00 PM - Doors open, Welcome

6:10 PM - 6:40 PM : Introduction to KNIME, Demo of KNIME Analytics Platform and Q&A

6:40 PM - 7:10 PM : Introduction to fraud detection techniques, LLMs and the KNIME AI Extension (Labs) and Q&A

7:10 PM - 8:40 PM : Enrich Data Analytics for Fraud Detection with GenAI. We'll open KNIME on our laptops and build a KNIME workflow!

8:40 PM - 9:00 PM: Networking and refreshments

Meet the speakers:

Roberto Cadili : Roberto is a data scientist on the Evangelism team at KNIME. During his BSc. in Economics, he developed a genuine interest in statistics and data analysis. At the University of Konstanz, he pursued a MSc. in Social and Economic Data Science where he studied different machine learning algorithms and deep learning architectures with an emphasis on NLP and Computer Vision. As editor for Low Code for Data Science, he is helping the KNIME community shape successful data science stories, tutorials, and best practices that are worth sharing.

Lada Rudnitckaia : Lada is a Data Scientist on the Education team at KNIME and holds a master's degree in Data Science. She specialises in developing and teaching courses on data science and engineering with KNIME and is interested in explainable and responsible AI, machine learning models deployment, and data engineering.

Onsite Address:
Room info:
https://essroombook.fas.harvard.edu/room.php?rm=rm0308
CGIS-South Concourse Level,
1730 Cambridge Street Cambridge MA 02138