MLOps: Managing Data and Workflows for Efficient Model Development and Deployment

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 (labeling, 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.