Data Science for the Modern Data Architecture

Today, data scientists are leveraging data science and machine learning algorithms to solve complex predictive analytics problems. Some examples of these problems include predictive maintenance, churn prediction, entity matching, and image classification. Though everyone wants to forecast the future, fully exploiting data science for predictive analytics remains the domain of only a few. To expand the reach of data science (the most in-demand skill), the modern data architecture needs to meet the following requirements:

·         Bring predictive analytics to the IoT Edge

·         Enable applications to consume predictions and become smarter

·         Become faster, easier, and more accurate to deploy and manage

·         Fully support data science life cycle

 

Data Smart Applications

You might know that end-users consume data, analytics and the outcomes of data science analytics through data-centric apps. However, a large number of applications today don’t fully leverage predictive analytics, data science, and machine learning.  A new generation of consumer and enterprise facing apps are being built to leverage data science/predictive analytics and deliver context driven insights to push end-users to the next set of actions. These apps are known as data smart applications.

Faster, More Precise, and Easier Management

Today business enterprises are collecting ever larger datasets, running more compute intensive machine learning and deep learning algorithms, all across bigger compute clusters. This needs a sophisticated and mature big data and big compute platform. The compute platform needs to fully exploit hardware advances and make them accessible to data smart applications and big data analytics. Hardware advances such RDMA, GPU, and FPGA need to be made available to the compute framework with an accurate level of resource sharing and isolation semantics. Also, you might know that a lot of data science workloads exploit R packages and Python libraries. Managing these dependencies in a disseminated cluster is not an easy task.

Smarter Edge

The Internet of Things, commonly known as IoT, is growing very fast and the market size estimates are enormous. Recently, the International Data Corporation (IDC) has predicted that global IT spending on IoT devices or objects will reach $1.29 trillion by the end of 2020. Edge Intelligence has the ability to render precise predictions and insights where required most, at faster speeds, without even requiring a resolute network connection. What actually is needed is to deliver precise predictions at the edge. 

In the end, we can say that the modern data architecture platforms are evolving to democratize data science. If you also want to be a part of the big data world, we would suggest you do a masters in data science in India from any good institute.