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Becoming a Machine Learning Engineer – The Fundamentals

On May 11 1997, something unusual took the world by storm. It was a game of chess.

Not the usual suspects

For one, it was a defining moment for mankind because the game took place between the most unlikely of opponents. Garry Kasparov, reigning world champion was on one end and on the other was IBM’s Deep Blue – a pathbreaking chess-playing computer developed by the behemoth. It was Man vs. Machine.

The outcome?

Deep Blue defeated the grandmaster, much to everyone’s surprise. It was the arrival-of-artificial-intelligence moment that signified a simple fact – a machine could match human intelligence.

While Artificial intelligence had been making inroads well before this, it was the first time that machine intelligence was displayed in all its glory, and so famously. It’s worth noting that this genius machine was the predecessor of the ones currently available, which makes one wonder about the superior processing powers of today’s systems.

Today, when AI has become a part of all aspects of our lives (and industries) there’s a lot more to it than we may realise. It can, for instance, help make beer (true, this is).

Explain Artificial Intelligence to me

Artificial Intelligence is used to represent machine learning technology, which is at the core of technology today. In fact, machine learning is generally considered to be a part of artificial intelligence.

Machine learning (ML), in simple terms, is the implementation of algorithms that are capable of learning by themselves based on training and experience. However, while machine intelligence isn’t (yet) capable of imitating human intelligence in its entirety, it can perform complex computation that can ease things considerably. Systems that can read better than humans, those which can detect errors in legal documents, calibrate phone cameras to enhance picture quality, personalise content based on user preferences, improve vaccine delivery, and tireless robots that can automate any type of processes in large warehouses are some of the applications where AI has stumped us.

ML is still evolving and it requires a lot of people with the right skills to help it become even more streamlined yet powerful. People need not be apprehensive about machine intelligence as the technology isn’t as developed as one might think it is. Machines are in no way as intelligent as humans yet, nor are they capable of cognition at such high levels, as of today.

Machine intelligence can be seen in a variety of applications – from social media platforms to ticket reservation systems. It is used in every place that requires a lot of data to be processed or in such scenarios where systems need to make their own decisions. Possessing the right ML skills can open up many opportunities that can lead to working on pioneering innovations in the field of machine intelligence.

How do you become a ML engineer.

To know how to become an ML engineer, it is important to know what an ML engineer does in the first place. An ML engineer is someone who makes use of their expertise in subjects such as mathematics, programming and data science to understand the type of machine learning technique to utilize in order to address specific problems related to a particular domain.

The engineer must also be able to recognize patterns in datasets, and create and maintain intelligent models that are capable of parsing the datasets to produce meaningful interpretations. ML engineers understand software development methodology and the full range of tools. In fact, it is quite similar to what a data scientist does. However, data scientists come up with meaningful insights about different datasets based on the scenarios they are used in while ML engineers create models and softwares that predict and control outcomes.

Let’s take a look at the list of requirements to become a machine learning engineer.

  • One needs to understand statistics and probability.
  • One must train to become a software engineer by getting a relevant education including a bachelor’s, master’s or a doctoral degree.
  • Get some first hand experience in data science by learning how to obtain, filter and optimize datasets to produce meaningful output.
  • Gain some experience in data modeling and evaluation.
  • Learn how to create data pipelines in order to obtain data.
  • Improve data visualization skills.
  • Learn about different machine learning techniques such as linear regression, logistic regression, decision trees, support vector machines (SVM), naive bayes etc. Each of these algorithms cater to different types of problems.
  • Improve software development skills by learning to program in languages such as Python, C, C++, Java, JavaScript, R, MATLAB, SQL etc. Learning how to program in different languages can improve your chances.
  • Get relevant ML experience by experimenting with different programs on various ML platforms.
  • Build, test and maintain ML models.
  • You’ll need to learn how to prune, analyse and filter results in order to improve your ML model.
  • Improve documentation skills.
  • Keep oneself updated with the latest ML technologies and advancements.
  • Make use of online resources to enhance skills.
  • Learn how to create interfaces so that the people who use your models can read the results easily.
  • Improve people skills. You’ll have to work with a lot of people to develop models that cater to particular problems.
  • Practise! Practise what you’ve learnt and strive to get better at developing models.

The Future

The machine learning industry is expected to grow by USD 11.16 Billion During 2020-2024.


Source: businesswire.com

If you have the skills, the inclination and the acumen, this is an industry that guarantees a great career. Advancements in the field of machine intelligence will change the way that we perceive data and run businesses. Understanding how to read data is especially important in these times due to the large amount of information that gets generated by users everyday. Machine learning engineers are the need of the hour and are expected to have some of the best career prospects in the years to come.

Keep checking this space to know more about the best universities across the globe to study machine learning from!

Date added
28.06.2021

Filed under:

Global Employability

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