BARC – The impact of MLOps and DataOps on the success of applied Machine Learning

In this presentation, BARC analyst and Data Scientist Alexander Rode provides insight into BARC’s latest international study on the prevalence and perception of the concepts MLOps and DataOps. Are they a differentiating factor for the success and progress of Machine Learning (ML) adoption in enterprises? How widespread is their adoption and perception? What problems do enterprises face when applying ML? Which of these can or cannot be addressed by MLOps and DataOps?


Alexander Rode, Analyst Data & Analytics & Data Scientist | BARC

Alexander Rode is a Data & Analytics Analyst and Data Scientist at the Business Application Research Center (BARC). He advises companies on use case identification for data analytics / machine learning and on tool selection for advanced analytics. He conducts proof of concepts in the area of advanced analytics and gives data science and data literacy coaching. Alexander Rode is author of BARC market studies and research articles. He gives talks at conferences and conducts (in-house) seminars on behalf of BARC. He is largely responsible for the data management, data preparation and data enrichment of the BARC product and service overviews.