Introduction
According to a report titled, “Evolving Data Infrastructure”, the frequency of trends and technologies related to data science, machine learning and artificial intelligence would witness a sharp increase across various industries. Consequently, the need for data science and data analytics courses in India would become extremely crucial as India hosts one of the largest data markets in the world. The promotion of data science courses in India would serve two important purposes. Firstly, it will close the gap between academics and industry and secondly, it will lead to the foundation of future data technology in India.
In this article, we examine data science technologies that are slated to make a mark in 2022.
Digital twins
The new technology of digital twins has been creating a buzz in the data science market. With the help of this technology, it becomes possible to create a replica of physical systems in the digital world. This has two important implications. Firstly, it helps data scientists to create a digital replica to understand the functioning of the system before it can be replicated in the practical domain. By understanding the pros and cons of the digital twin, its feasibility of implementation can be examined.
Secondly, conceiving the digital twin of a physical object helps us to design and develop an advanced replica without the need of going through intermediate stages. This is because the intermediate stages can be executed in the digital domain itself. So, the final model that is conceived in the physical environment is a foolproof, technologically tested and research intensive outcome.
AI-as-a-service
The process of outsourcing artificial intelligence and allowing third parties to host it is becoming the new normal in industry 4.0. The advantage of outsourcing this service avoids the need for installing heavy machinery to run the service. In addition to this, it also allows us to reduce the upfront costs that are associated with establishing and maintaining this service. An example of AI-as-a-service can be understood from Amazon Web Services that facilitates the delivery of cloud based services, machine learning services as well as computer vision tasks.
AI-as-a-service is also provided by Google AI platform. This platform provides a programming interface that allows developers to simulate various types of data science projects in a pre-designed environment. The most important stage of the process is the programming stage. Once this stage is taken care of, other important stages like data processing, data modeling, model training and outcome prediction are taken care of by the interface itself.
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Ensemble models techniques
After machine learning models started to gain importance, their deployment became widespread. Over a period of time, the security aspects of such models began to be compromised. In addition to this, a debate also started over performance of one model face to face with the other. This is where the concept of ensemble model techniques gained a significant amount of traction. Ensemble model techniques deploy a large number of machine learning models simultaneously with the aim of increasing the performance of the final paradigm.
For instance, we may use various models like bagging, bootstrapping, K-nearest neighbour, K-means clustering as well as decision trees simultaneously to not only increase the performance of the overall model but also to increase the accuracy of the final outcome. Ensemble model techniques find application in various smart city environments where the likelihood of data breach is very high. In this context, ensemble model techniques can allow us to conceive an intrusion detection system that is immune from various types of viruses, trojans and malware.
The way ahead
The influence of data science certification bootcamp by Caltech technology in the age of industry 4.0 is simply overwhelming. This is due to the application areas of data science technology that include education, healthcare, supply chain management, logistics, agriculture, robotics, stock market as well as the financial market. Furthermore, this influence can also be gauged from the new gateway that has been formed by data science technology for expanding the research frontier in a multifaceted manner. Focus should also be laid on the convergence of various technologies and domains like artificial intelligence, machine learning, business analytics and cloud computing that has happened due to advancement in data science.
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The bottom line
The trends and technologies in data science will continue to expand undeterred in 2022 as well. Ranging from generative artificial intelligence to other technologies like deepfakes, the list is simply endless. While all these technologies continue to thrive under the umbrella of data science, the quest for new ones continues unabated.