During this conversation, I am joined by guest Gard Hauge, CTO of StormGeo, a weather prediction specialist and researcher with a background in software. We discuss Gard’s extensive research and its application at StormGeo, his historical experience with the company’s evolving relationship with machine learning, how weather and markets are related, and more. Touching on challenges in the field, my guest reveals the growing volume of data he deals with on a daily basis. We discuss the fundamental role of data engineering alongside machine learning, the key role of third-party data, and more before Gard shares his perspective on the future of StormGeo. He also delves into his experience to give informed advice to listeners. Join in to hear more from this thought leader today!
Key Points:
- Gard Hauge, CTO of StormGeo, shares his background and introduction to StormGeo.
- The topic of his Ph.D. research: weather prediction.
- Products and services offered by StormGeo beyond weather prediction.
- The evolving role of machine learning at StormGeo and how it is integrated today.
- How weather and markets are related.
- Investments StormGeo is making into generative AI.
- Gard’s relationship with data collection and processing.
- The biggest challenges he faces especially in relation to the volume of data.
- The fundamental role of data engineering in building successful algorithms.
- The key role of third-party data.
- Advice for other AI startup leaders.
- Gard’s predictions for the future of StormGeo.
Quotes:
“The data pipeline is something we put a lot of effort into developing over the last decade. Actually, streamlining how we actually process and make this data available in products and services is key.” — Gard Hauge
“The amount of data we face is typically doubled every two years. So, we need to be quite smart in handling and processing data and what we're actually archiving for machine learning.” — Gard Hauge
“Everybody talks about AI and machine learning. But our experience is that 80% to 85% of the work is basically data engineering, and that's a key fundament if you want to build successful algorithms.” — Gard Hauge
Links:
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