In this episode, Dean speaks with Michał Oleszak, an ML engineering manager at Solera. Michał shares insights into how his team is using machine learning to transform the automotive claims process, from recognizing vehicle damages in images to estimating repair costs. The conversation covers the challenges of deploying ML pipelines in production, managing data quality for computer vision tasks, and balancing technical implementation with business needs. Michał also discusses his approach to model evaluation, the benefits of monorepo architecture, and his views on exciting developments in self-supervised learning for computer vision.
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Timestamps:
00:00 Introduction
00:42 Production for Machine Learning at Solera
03:49 Transitioning from Images to Structured Data
04:58 Combining Deep Learning and Non-Deep Learning Models
05:15 Deployment Process for Machine Learning Models
08:01 Challenges and Solutions in Monorepo Adoption
12:57 Evaluating Model and Pipeline Versions
21:57 Tools for ML Projects: Monorepo, Pants, GitHub Actions
24:04 Data Management and Data Quality
30:14 Challenges in ML Efforts: Data Quality
30:37 Excitement about Self-Supervised Learning and JEPA Architectures
34:45 Controversial Opinion: Importance of Statistics for ML
36:40 Recommendations
Links
🌎Prisoners of Geography by Tim Marshall: https://www.amazon.com/Prisoners-Geography-Explain-Everything-Politics/dp/1501121472
➡️ Michał Oleszak on LinkedIn – https://www.linkedin.com/in/michal-oleszak/
➡️ Michał Oleszak on Twitter – https://x.com/MichalOleszak
🌐 Check Out Our Website! https://dagshub.com
Social Links:
➡️ LinkedIn: https://www.linkedin.com/company/dagshub
➡️ Twitter: https://twitter.com/TheRealDAGsHub
➡️ Dean Pleban: https://twitter.com/DeanPlbn
