EDITOR ’ S QUESTION

OMAR AKAR , REGIONAL VP FOR CEE AND META , PURE STORAGE

As-a-Service consumption models should be considered as an effective tool to increase the flexibility of the AI platform .

Data Science as-a-Service is what data scientists want to handle the demands of AI .

They will also enable the people building it to easily incorporate new solutions or change their infrastructure as required by the constantly evolving needs of data scientists . Essentially supporting all six of the steps detailed above .

Additionally , as-a-Service models enable organisations to meet their sustainability goals by better controlling energy costs through lower power consumption and by only using the resources that are needed at that time .

Some Storage as-a-Service offerings are also backed by SLAs to pay for electricity use , and they support sustainability goals by eliminating rip and replace tech refresh cycles and the e-waste they generate .

The data journey for AI is one of data amplification . This will require more and more infrastructure to support the development of future AI . Data Science as-a-Service is what data scientists want to handle the demands of AI . It means tools as well as data , provided on-demand and through automation .

Leading AI organisations are now building Data science as-a-Service platforms , leveraging a lot of the tools mentioned above built on software infrastructure such as Kubernetes .

To be successful though , these platforms need to provide not only the data frameworks and tools as-a- Service , but also the data itself , otherwise it negates the benefit of self-service .

Data platforms tightly integrated with Kubernetes and allowing easy sharing , copying , checkpointing and rollback of the data itself are key to success in this area .

With data scientists spending so much time preprocessing and exploring data , they need the tools , resources and platforms to conduct this work efficiently , as and when they need it . Python and Jupyter Notebooks have become the day-today language and tools for data scientists and the data ingestion , processing and visualisation tools all have one thing in common : they can be deployed as a container .

Achieving it requires the right software and hardware infrastructure , combined with the right consumption model in order to make it a success and take an organisation from data ingestion to innovation .

The ideal platform for data scientists to do all they need is therefore one that will support all these tools , enable them to deploy and run containers quickly and easily and most importantly in a self-service manner .

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