I am currently a Machine Learning Engineer at Yanmar R&D Europe.
I previously transitioned from the world of aftersales to data science in early 2020 and got my first role as a data scientist at la Marzocco, where I built ETL pipelines on AWS and managed the cloud infrastructure with Terraform. A year and a half later, I joined Yanmar and have been working on applying machine learning in the engine and powertrain sector.
I value code quality and have a bit of a soft spot for software design.
This discussion session focuses on exploring the application of software engineering practices in the field of data science. Join us to delve into essential aspects such as python packages, IDEs, testing, refactoring, and architecture that play a crucial role in building robust and scalable data science solutions. We will discuss how adopting software engineering principles can enhance the reliability, maintainability, and efficiency of data science projects. Whether you're a DS manager or practitioner, this session offers a platform to exchange insights, share experiences, and discover innovative approaches to integrating software engineering practices into the data science workflow.