01963nas a2200313   4500000000100000000000100001008004100002260000900043653002800052653001300080653001600093653001800109653002100127653001500148653001700163653003000180653002100210100001500231700001900246700001800265700001400283700001900297700002100316700002000337245004600357300001200403490000700415520122700422       2020                            d  c202010aartificial intelligence10abig data10acharge back10adata engineer10adata engineering10adata range10adata science10adata science as a service10amachine learning1 aPeter Lenk1 aMichael Street1 aIvana Mestric1 aArvid Kok1 aGiavid Valiyev1 aPhilippe Le Cerf1 aBarbara Lorincz00aData Science as a Service: The Data Range  a157-1710 v473 a<p>As with many new disciplines, in many organisations data science is being embraced in a piecemeal way with many parts of organisations creating special purpose environments designed to answer specific problems, fragmenting the overall capacity and knowledge base. Often vendors selling proprietary approaches, potentially creating lock-in, fuel these isolated solutions. This article’s main contribution is a ‘Data Science as a Service (DSaaS)’ model, where common elements required to conduct data science are abstracted and gathered into a logical layered, service-based architecture. This way, each element of the organisation can utilise the services it needs to progress its work, use specific solutions or share common tool sets, share results in a ‘model zoo,’ share data sets, share best practices and benefit from common, robust high-performance infrastructure and tools. With such an approach, it is possible to cluster data science skill sets and provide critical mass where needed. The proposed approach also facilitates a charge-back business model, where data science services are costed and charged to internal organisational elements or external customers in a measured, pay-as-you go way.</p>