Five Unexpected Considerations when Implementing Machine Learning Operations
Conclusion:
Implementing machine learning operations (MLOps) is complicated by several challenges: the number of the stakeholders involved in a project; the shortage of people with the necessary skills; the scope of regulatory compliance; validation of the machine learning (ML) model; and model degradation issues. Considering how these challenges will be addressed is a vital precursor for the successful implementation of MLOps.
About The Advisor
Joseph Sweeney
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.