Are you a student? If the answer is yes, you’ll know that one of the biggest decisions you’ll ever make is choosing the right career. However, before you make a career choice, it is important to know the pros and cons of machine learning – the career you want to pursue. This article will introduce you to the pros and cons of machine learning engineering – an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Pros and Cons of Machine Learning:
 Great Opportunity for Progress
Machine learning has made tremendous progress in the recent decade. The growth is enormous and very diverse, slipping its tentacles into a variety of industries – from finance and healthcare to transport and education. As a machine learning engineer, you will not only witness the technological revolution but be a part of it! Additionally, machine learning engineering as a career is an extremely lucrative job. The starting salary of a machine learning engineer in India is INR 6,70,000 per year! The mid-career salary of a person working in this field is a whopping Rs 1,568,971.
 Work that matters
Machine learning engineering will allow you to work and build real-world products, right from autonomous cars to security drones. These are not some numbers you crunch into a spreadsheet only to never hear about them again. Everything you create has a real-world application. Imagine the satisfaction of seeing something you’ve created help someone in their everyday life! To put it simply, the efforts you put in day in and day out is for work that matters.
 Direct link to Data Science
As a machine learning engineer, you will also develop the skills needed to be a data scientist. Becoming competent in both fields will make you a hot commodity for employers. As a data scientist, you’ll be able to analyze data and extract value from it. As a machine learning engineer, you’ll be able to make use of that information to train a machine learning model to predict results. In several organizations, machine learning engineers work with data scientists for better synchronization of work products.
 Demanding Job
Training models, handling data as well as making and testing prototypes on a daily basis can lead to mental exhaustion. As a machine learning engineer, data munging will also be a painful part of your job. Data munging simply means converting raw, unprocessed data into a more appropriate, usable form. Sometimes you might even have to scrape data from a paginated website and integrate it with your client’s internal data while simultaneously dealing with date-time and data type errors. Doing this is no walk in the park and it could get frustrating for some.
 It takes time and resources for machine learning to yield tangible results
Machine learning occurs over time. So, there will be a period when your interface or algorithm won’t be developed enough for your company’s needs. The precise amount of time required will depend upon the nature of data, data source and how it is to be used. You’ll simply need to wait as new data is generated — sometimes this can take days, weeks, months or even years!
 Need to stay updated
As mentioned earlier, machine learning is a rapidly evolving field. Due to this, machine learning engineers are required to spend a considerable amount of time learning about the latest updates in the field. Reading and learning research papers from various universities and organizations will have to become a regular part of your life if you want to pursue this field. So, unless the idea of continuous learning does not appeal to you, you should rethink your decision of being a machine learning engineer.
It is best to first know both sides of the coin. Research well about the options in front of you and then make an informed choice. Weigh the pros and cons of machine learning before you decide whether or not to make a career in this field.
Since you are here, take a look at A day in the life of a Machine Learning Engineer