Six Critical Success Factors for Machine Learning Projects
The deployment of machine learning (ML) solutions across a broad range of industries is rising rapidly. While most organisations will benefit from the adoption of ML solutions, ML’s capabilities come at a cost and many projects risk failure. Deployment of ML solutions needs to be carefully planned to ensure success, to minimise cost and time, but also to deliver tangible results and assist decision-making.
About The Advisor
Dr. Philip Nesci is an IBRS advisor specialising in digital transformation, Cloud strategy and analytics, cyber resilience and risk management, and large scale program management. Philip has an extensive track record as a CIO and an Executive in global commercial organisations such as Shell, Orica and China Light and Power, where he has orchestrated and delivered major organisational transformations enabled by technology. More recently as CIO of Monash Health and the Australian Red Cross Blood Service, Philip has focused on the Health sector and in Government leading a number of programs which have significantly reshaped the customer experience and engagement, underpinned by cyber resilience. Philip’s approach to strategy development and implementation is achieved through strong leadership and extensive engagement with Boards and Executives. Philip’s blend of business and technology experience across a wide range of industries and enhanced by working extensively in Australia, Europe, Asia and the USA, provides him with unique understanding in successfully planning and executing digital strategies to reshape business.