Machine learning operations (MLOps) is the engineering field focused on techniques for developing and deploying machine learning solutions at scale. As the name suggests, MLOps is a combination of machine learning development (“ML”) and software/IT operations (“Ops”). Blending these two words is particularly complex, given their diverse nature. ML development is characterized by research and experimental components, dealing with large amounts of data and complex operations, while software and IT operations aim at streamlining software deployment in products. Typical problems addressed by MLOps include data management (labelling, organization, storage), ML model and pipeline training repeatability, error analysis, model integration and deployment and model monitoring. In this talk, we’ll present practical MLOps techniques useful for tackling a variety of MLOps needs. We’ll illustrate these techniques with real-world examples from our work developing autonomous flying capabilities as part of the Wayfinder team at Acubed, the Silicon Valley innovation center of Airbus.
Carlo Dal Mutto is an engineering lead with extensive experience in computer vision, machine learning and robotics. His experience spans between algorithms, software, hardware, system design and implementation. He has a solid track record of successful product deliveries throughout my experience at Aquifi and Acubed (the Silicon Valley innovation center of Airbus). He is an inventor of more than 25 patents, he has co-authored several books and research papers with more than one thousand citations. He has been invited speaker and technical committee member at world-class AI conferences.
Konstantinos Balafas is the Head of AI Data for Wayfinder, focusing on streamlining Wayfinder’s data operations and developing tools and products that enable the extraction of insights from collected data. He has 6 years of experience as a Data Scientist in the consulting and automotive fields, where he led small teams of Data Scientists in developing Machine Learning solutions to address business problems. He holds a Ph.D. in Structural Engineering from Stanford University.
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