Automating the tasks using machine learning empowers various teams and departments to automate the development of processes to eliminate repetitive work. Without machine learning automation, the process development can take up to months, from data processing to training, until the actual deployment. 

Various machine learning automation tools have been introduced to pace up the process development pipeline. At times, this refers to automating just specific tasks. In other cases, it means automating your whole set of processes. In this article, we examine the potential and possible ways of automating tasks with the help of machine learning.

Physical Labor Tasks

Physical labor tasks in the industrial, as well as public sector, have the greatest potential for automation. Automated machines such as industrial robots are far superior to people at performing physical labor tasks as they don’t get exhausted and can perform repetitive tasks hassle-free. Nonetheless, unpredictable tasks i.e. the ones involving human intelligence, require the human level of adaptability in accomplishing the tasks that are as yet not available to machines. 

Most probabilities of automation jobs require lower education levels and include redundant tasks. Machines are fed the required data to learn and train through machine learning models in order to imitate human behavior. This is very much expected while redundant tasks provide a predictable environment to the machines and they can effectively perform low-expertise tasks restlessly.

Data Entry Tasks

Machine learning is often compared to artificial intelligence and data mining as both include the way to recognizing the patterns in data. In any case, the significant difference lies in the way that data mining includes data extraction for analysis by humans, while machine learning algorithms understand and learn patterns from the data without any human intervention, which would then be able to be utilized for data entry. 

A basic model would be the word suggestions you get on your smartphone keyboard while you are texting. These word suggestions are made with the help of inputs to the past messages and predict what you are trying to say at that point. When a machine learning algorithm is utilized for a data entry program, a similar methodology will be used to recommend the data to be entered, which depends on the information entered previously.

Software Development Tasks

With the rise of machine learning, the phenomenon of automating software development has also emerged. Software is getting on an increasingly higher level. Earlier, programming with Assembly, C, and other essential programming languages was an ultimate flex. However, in today’s consistently advancing world, another paradigm is coming in. And, that paradigm is none other than gradient-based methodologies. 

Back in time, we used to create all the logic in our projects, but now since we’re utilizing gradient-based approaches and machine learning and automation, we can start handling more complex tasks. An incredible illustration of this is self-driving vehicles. Simply 10 years ago, this would have been impossible to achieve, yet now, rather than making the logic all by ourselves, we train the models that then make the heuristics. These heuristics help with automating the entire process of driving the vehicle through extensive machine learning and deep learning algorithms.