Options for Machine Learning-as-a-Service: The Big Four AIs Battle it Out
Conclusion:
As-a-Service machine learning (ML) is increasingly affordable, easily accessible and with the introduction of self-learning capabilities that automatically build and test multiple models, able to be leveraged by non-specialists.
As more data moves into Cloud-based storage – either as part of migrating core systems to the Cloud or the use of Cloud data lakes/data warehouses – the use of ML as-a-Service (MLaaS) will grow sharply.
This paper summarises options from four leading Cloud MLaaS providers: IBM, Microsoft, Google and Amazon.
About The Advisor
Dr. Philip Nesci
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.