Machine learning operations: AI surgical strike or bloody mess?
Conclusion: Machine learning operations (MLOps) adapts principles, practices and measures from developer operations (DevOps), but significantly transforms some aspects to address the different skill sets and quality control challenges and deployment nuances of machine learning (ML) and data engineering.
Implementing MLOps has several benefits, from easing collaboration among project team members to reducing bias in the resulting artificial intelligence (AI) models.
About The Advisor
Dr. Joseph Sweeney is an IBRS advisor specialising in the areas of workforce transformation and the future of work, including; workplace strategies, end-user computing, collaboration, workflow and low code development, data-driven strategies, policy, and organisational cultural change. He is the author of IBRS’s Digital Workspaces methodology. Dr Sweeney has a particular focus on Microsoft, Google, AWS, VMWare, and Citrix. He often assists organisations in rationalising their licensing spend while increasing workforce engagement. He is also deeply engaged in the education sector. Joseph was awarded the University of Newcastle Medal in 2007 for his studies in Education, and his doctorate, granted in 2015, was based on research into Australia’s educational ICT policies for student device deployments.