Machine learning is eating the world, and spilling over to established disciplines in software, too. After MLOps, is the world ready to welcome MLGUI

Building MLGUI, user interfaces for machine learning applications

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2021-07-20 13:00:43

Machine learning is eating the world, and spilling over to established disciplines in software, too. After MLOps, is the world ready to welcome MLGUI (Machine Learning Graphical User Interface)?

Philip Vollet is somewhat of a data science celebrity. As the senior data engineer with KPMG Germany, Vollet leads a small team of machine learning and data engineers building the integration layer for internal company data, with access standardization for internal and external stakeholders. Outside of KPMG, Vollet has built a tool chain to find, process, and share content on data science, machine learning, natural language processing, and open source using exactly those technologies.

While there are many social media influencers sharing perspectives on data science and machine learning, Vollet actually knows what he is talking about. While most focus on issues of model building and infrastructure scaling, Vollet also looks at the user view, or frameworks for building user interfaces for applications utilizing machine learning. We were intrigued to discuss with him how building these user interfaces is necessary to unlock AI’s true potential.

Vollet and his team build data and machine learning pipelines to analyze internal data and work on reports for KPMG’s management. They implement a layer enabling access to data and build applications to serve this goal. The first question to address when it comes to building user interfaces for machine learning applications is, are those applications different from traditional applications, and if yes, how?

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