The Netherlands, Aizonic
Bas is a technology leader in the AI and big data domain. His academic background is in Artificial Intelligence and Informatics. Trained as a software engineer and architect, he has 15 years experience in delivering succesful data-driven projects with a wide range of companies and technologies. He occasionally teaches programming courses and is a regular speaker on conferences and informal meetings, where he brings a mixture of market context, his own vision, business cases, architecture and source code in an enthusiastic way towards his audience.
The State of MLOps – Machine Learning in Production at Enterprise Scale
Artificial intelligence (AI) has quickly become the main focus topic for organisations and governments worldwide. What started in small R&D environments in the ‘big data’ revolution a few years ago has now grown into a mature practice where data scientists and data engineers work together towards common business goals. AI is powering the finance, retail, energy, and healthcare sectors. This growth also comes with challenges; machine learning models cannot live on their own and have to be incorporated into a production environment. To that extend programming frameworks, tools and infrastructure are evolving at an enormous pace. New architectures and design pattern have arrived to work with these new technologies. One important field of research is MLOps, which has evolved into a way of working and set of best practices to deploy, test, manage, and monitor machine learning models in production. In this session, we’ll explore this relatively new subject. Bas will explain the need for MLOps (and AIOps and ModelOps which are related), dive into the tools and techniques, and give some examples of real-world solutions.