Today, the extent to which technology is being deployed in almost every possible thing is worth mentioning. No wonder new technologies come into existence every day and hence open the door for several career opportunities. Having said so, one of the most sought-after career options these days is in the field of machine learning. The importance of a machine learning engineer can merely be put into words, and this is why people across the world try their best to become one. What exactly is machine learning, and why is it critical, is something to be understood in the first place.
What is machine learning?
A few decades back, technology wasn’t advanced enough to cater to business needs and address the various problems. But with the advent of Artificial intelligence (AI), things have changed for the better. Gone are the days when AI was thought to be a thing of the future, something that future generations might get to witness. Well, if only things worked as predicted. Today is the world of Artificial intelligence, the world we are living in!! No wonder AI has carved a niche for itself in the past few decades, and the results put forth by it have been phenomenal.
Machine learning is one such branch of AI. Machine learning centers on the improvement of PC programs that can access data and use it for learning. It mainly focuses on building applications that learn from data and improve their accuracy over time. Well, it doesn’t end here. All this – without being programmed to do so. How amazing is that? Wondering how? The algorithms in machine learning are already trained to find the trends and patterns in the data that make decision-making easy, without a doubt.
All this requires a machine learning model to be deployed. It is where machine learning engineers come into play. They make combined use of analytical and data science knowledge to design/develop various machine learning systems. These engineers’ main aim is to create or establish specific software components that can work with minimal human supervision and further help draw meaningful conclusions from the data they are provided with.
The number of machine learning engineers is not very high, but now that people know how promising this career option is, the numbers increase. In addition to getting to learn a lot in this field, one gets paid handsomely as well. Hence, this has to be one of those career opportunities that garner attention from every corner of the world.
What are the skills acquired by the machine learning engineer?
Now that machine learning as a career option has been explored let’s look at what skills are acquired by machine learning engineers while enjoying this promising career thoroughly.
Machine learning does require one to have some programming skills. Languages like Python, R, C++, Java, etc., have a lot to do with how a statistical model is deployed. Almost all the algorithms make use of these languages in one way or the other. Over time, while working on different projects, someone working in machine learning can expect enhanced programming skills, without a doubt.
No doubt, this is another aspect without which machine learning holds no value. Wondering how? One can apply mathematical formulas in selecting the correct ML algorithm for the data collected, can use mathematics to set parameters, approximate confidence levels, and a lot more. Topics like linear algebra, calculus, probability, statistics, distributions like Poisson, normal, binomial, etc., are applied in the development process, and these skills are sharpened with time.
Data modeling and evaluation
Data modeling is something that a machine learning engineer will have to deal with regularly. Create models using algorithms is the prime objective here. The engineers acquire this skill with every assignment that’s taken up. There’s no limit to getting knowledgeable, which is why this skill gets sharpened with every passing day. On the same lines, evaluating how good the model is yet another skill that gets enhanced and that too without putting in much effort.
Application of machine learning algorithms and libraries
A statistical model, be it a decision tree, a neural net, or any model for that matter, is successful only when the algorithms incorporated are backed by solid research and expertise. Hence, machine learning engineers acquire this skill wherein they can apply suitable algorithms with the help of which it is possible to draw meaningful conclusions. All of this is done using libraries sci-kit-learn, Theano, Spark MLlib, H2O, TensorFlow, etc. It paves the way for engineers to upskill their knowledge in these areas.
Let’s Sum Up
Above are a few of the many hard skills acquired by machine learning engineers. But, the extent to which soft skills see an improvement is worth an appreciation. Out of all the soft skills, communication skills are taken to a different level altogether. A machine learning engineer’s responsibility is not limited to transforming data to be able to draw insights. Conveying the same to the non-technical team, management, etc., is another crucial duty to perform. It ultimately throws light on how good the communication skills turn out to be eventually over some time.