Morphology and life cycle of data science platforms

Data science platform market is slated for an exponential growth of more than 40% in the next two years. A forecast by market research predicts numbers as high as 66% of growth in the next 3 years. All this gives an account of the intensity of the data science market as well as the tools that are involved there.

Data science platforms have a wide range of applications when it comes to new and emerging businesses. It also caters to the requirements of the established business and helps them to derive state-of-the-art analytics. In this article, we aim to understand the entire morphology and life cycle of data science platforms in detail.

Data science platform: Morphology

In simple terms, we can define a data science platform as a technical framework that enables us to understand the working of the data science life cycle. In most cases, the data science life cycle revolves around three important stages. The first stage is called the stage of data integration. Needless to mention, the data that is fed into this stage is already processed and cleansed. It is structured in format. The second stage is all about the development of the data science model that caters to the requirements and the given set of parameters. The third stage is related to the deployment of the data science model that has been developed in the second stage.

With the help of a data science platform, we can effectively track the changes in the life cycle of the data science project that we are working with. This helps us to derive analytics with a lot of ease. The deployment of models also requires a rapid pace using the data science platform.

 

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