Our guest this episode is Alexander Schober, a data & AI project owner at Motius. He manages a diverse team of tech experts, focusing on Machine Learning, Knowledge Graphs, and Data Analysis.
Alexander previously worked at Siemens Technology which involved pioneering research in Federated Learning and Self-Supervised Methods for anomaly detection. He used algorithms like Federated Averaging and SimCLR to address data privacy and label sparsity. Alexander joins Seth Earley and Chris Featherstone to the discuss knowledge graphs, metadata modeling for data engineering, using large language models to build data pipelines and more.
For more content related to LLM's and Knowledge Graphs: https://www.earley.com/case-studies
Takeaways:
- AI Enhancements with Knowledge Graphs: While not strictly required, knowledge graphs enhance the capabilities of AI, particularly large language models. The ability to provide context and resolve conflicts within the data contributes to more accurate and reliable AI outcomes.
- Unified Metadata Model: There's a need for a unified metadata model across different tools and platforms in the data engineering and AI landscape. Disjointed metadata tools can lead to inefficiencies, and efforts should be made to integrate and unify metadata for better collaboration.
- AI-Powered Data Pipeline Construction: Large language models can be used to generate data pipelines based on provided metadata. This approach can streamline the data engineering process, ensuring that quality checks, governance attributes, and privacy classifications are integrated into the pipeline.
Quote of the Show:
- " All of these things are interconnected. Knowledge graphs, ontologies and semantics. They are all very important."
- Alexander Schober
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