MLOps 2.0 (advanced ML operating processes) is a term used in the fields of artificial intelligence, automation, big data and smart data. It describes the modernised way in which companies organise and improve the operation of machine learning (ML) models. While MLOps originally helped to develop and manage ML models efficiently, MLOps 2.0 goes one step further: additional processes, tools and security measures have been introduced to make collaboration between data scientists and IT teams even easier, faster and more secure.
A simple example: an online retailer uses an ML model to make personalised product recommendations. With MLOps 2.0, the company can continuously monitor this model, improve it automatically and import updates with little effort. Errors are recognised and rectified more quickly and data quality remains protected.
For decision-makers, this means that with MLOps 2.0, intelligent applications can be operated more reliably and scalably - in line with the requirements for security, efficiency and innovation. This saves time, reduces costs and makes companies more competitive.
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