0
Software engineering for machine learning: a case study Amershi et al., ICSE’19
Previously on The Morning Paper we’ve looked at the spread of machine learning through Facebook and Google and some of the lessons learned together with processes and tools to address the challenges arising. Today it’s the turn of Microsoft. More specifically, we’ll be looking at the results of an internal study with over 500 participants designed to figure out how product development and software engineering is changing at Microsoft with the rise of AI and ML.
… integration of machine learning components is happening all over the company, not just on teams historically known for it.
A list of application areas includes search, advertising, machine translation, predicting customer purchases, voice recognition, image recognition, identifying customer leads, providing design advice for presentations and word processing documents, creating unique drawing features, healthcare, improving gameplay, sales forecasting, decision optimisation, incident reporting, bug analysis, fraud detection, and security monitoring.
As you might imagine, these are underpinned by a wide variety of different ML models. The teams doing the work are also varied in their make-up, some containing data scientists with many years of experience, and others just starting out. In a Continue reading