Automation: The Future of Data Science and Machine Learning?

Machine learning has been one of the most significant innovations in the field of computing, and now it is considered to be capable of taking on important roles in the field of big data analytics. Big data analysis is a massive challenge from the business perspective. For instance, activities like making sense of huge volumes of diverse data formats, data preparation for analytics and filtering redundant data can consume a lot of resources. Hiring data scientists is also an expensive proposition and not within every organization’s budget. Experts believe that machine learning can automate various tasks related to analytics- both complex and routine. Automating machine learning can free up many resources that can be utilized in more complicated and innovative jobs.

Automation in the Context of Information Technology

In the context of Information Technology, automation is the linking of different software and systems so that they can do particular jobs without any human interference. In the IT industry, automated systems can accomplish both simple and complex tasks. To perform its job, an automated system needs to be given explicit commands. When an automated system is required to alter the scope of its jobs, the program needs to be updated by a human being. While automated systems are competent at their jobs, errors may occur due to several reasons. When errors occur, the root cause needs to be recognized and rectified. Obviously, to do their jobs, automated systems are entirely dependent on human beings.

Usually, regular jobs are allocated to automated systems. A typical example of automation in the IT industry is automating the testing of web-based user interfaces. Test cases are fed into automation scripts, and user interfaces are tested accordingly. 

The argument in favor of automation has been that it completes routine and repeatable tasks and frees up workers to do more complex and creative works. Though, it is also argued that automation has displaced a lot of jobs or roles previously performed by humans. Now, with machine learning finding its way into numerous industries, automation could add a new aspect altogether.

Is Automation the Future of Machine Learning?

The crux of machine learning is the capability of systems to continually learn from data and evolve without the interference of human beings. Machine learning can behave like the human brain. Given this ability, machine learning is considered ideal for automating complex tasks related to big data and analytics. It has already overcome the critical limitation of the traditional automation systems which cannot work without regular human intervention. There are several case studies to reveal that machine learning can complete sophisticated data analysis tasks.

As already pointed out, big data analysis is a challenging proposition for organizations, and it can be partially given to machine learning systems. From the viewpoint of a business, this can bring a lot of advantages such as freeing up of data science resources for more creative and vital assignments, higher volume of work completion, less time taken to complete tasks and cost-effectiveness.

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