Key Insights (and Frustrations) Alongside the Technical Journey from a Fluffy AI Vision to a Complete ML-Product, Viewed from a Gritty Data Scientist Perspective
How do you go from a fluffy AI idea to a concrete ML product? The ambition is to clarify this fluffiness based on experiences from a completed project at Stena Stål with an end-to-end implementation predicting the volatile prices of steel. Focusing on key learnings from a data scientist’s perspective, we present an effective constellation of well-established technical solutions alongside emerging ML tools, such as MLflow, to produce a stable, repeatable and quality-assured machine learning workflow.
After covering the technical part, we will also try to give input on how to solve the really tricky part – how to intrigue stakeholders by mastering the art of explaining the concept of ML and how to stop wasting people’s skills and instead let them do the job they actually thrive in. Because then the ML-stars are aligned!
Andrea has been working in the data science field for over 4 years and has worked with everything from clustering driving behaviors, reliability tools, anomaly detection, prediction of future price trends to explaining the business value of ML-solutions to stakeholders. She has a master’s in mathematical statistics and has a passion for algorithms. Today, Andrea is a data scientist consultant helping clients build and understand intelligent solutions to enhance their products.
