Operationalizing the AI Lifecycle

Matthias Biniok


In this session, Matthias Biniok will talk about what comes after successfully implementing an AI solution. How do you keep track about the results your AI solution is producing? How do you continuously evolve your machine learning model? How do you leverage all the data that is going into your production environment? Using real-world examples and IBM Watson technology, Matthias Biniok will give you an overview about the necessary actions to take in order to improve your AI continuously.


Photo of Matthias Biniok

Matthias Biniok studied computer science and IT management in Jena, Tel Aviv and Frankfurt am Main. He worked in several departments at IBM, e.g. as project manager in transformation projects in the banking industry. Because of his high interest in artificial intelligence Matthias started working as Cognitive Solution Architect at IBM Watson in Munich. In December 2017 Matthias was appointed Lead Watson Architect for the DACH region. He is project lead and AI architect of Project CIMON (sending an autonomously free-flying robot with AI powered by IBM Watson to the international space station).

Requested 2 times

Lecture languages



AI / AutomationCloud & DataData Science / Machine Learning

Duration options

1 hour

Travel/delivery options

In-countryOutside of country: Open for discussionRemote via video conference



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