Why Is It The Right Time To Pursue A Career In Machine Learning Engineering?

As per a report, the global machine learning market will reach Rs 543 billion valuation by 2023.
Why Is It The Right Time To Pursue A Career In Machine Learning Engineering?

Machine learning will bring about not just a new era of civilisation, but a new stage in the evolution of life on earth.”

Pedro Domingos, Professor Emeritus of computer science and engineering at the University of Washington.

Machine learning (ML) has been a truly disruptive force, pivotal in ushering in the fourth industrial revolution. Forward-looking retailers, automotive players, financial services firms, game developers, researchers, etc. have taken to this AI technology like a duck to water. As per a report, the global ML market will reach Rs 543 billion valuation by 2023. Below is a graph representing the demand & supply gap in AI and Big Data Analytics talents from Nasscom.

The job openings during the 2018-2021 period have increased more than 64%, while the supply gap has widened by almost 125% – the figures speak for themselves.

ML on the rise

Machine learning has become ubiquitous – be it fantasy sports, or driverless cars from Google, or song recommendations in Spotify, our lives are immersed in ML.

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Machine learning structures our industries that rely on large amounts of data and need a framework to analyse it to optimise outcomes. AWS uses ML For detecting abnormal machine behaviour, Disney for animation, researchers for finding new planets; the use cases are plenty. 

Starting a career in machine learning engineering

First things first. Before you take the plunge, it’s crucial to have good background knowledge on the subject. For that, look out for free learning resources and books on machine learning, gain insights through interviews to have an overall understanding of the ML domain, etc.

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“My advice would be to not bind yourself to any tool/frameworks as frameworks and tools will come and go. Try to understand the mathematical concepts and the logic behind a data science algorithm and you should be fine.” – Rahul Aggarwal, ML engineer at Facebook.

Explore his interview here

Research Scientist and Research Engineer are popular variants of machine learning engineers. To have a career in machine learning engineering, yu should research new data methods and algorithms, such as supervised, unsupervised, and deep learning techniques, used in adaptive systems. 

However, a strong understanding of one or more of the following topics is a must:

  • Mathematics, including Calculus, probability and linear algebra, is required to make standard models.
  • Computer science to understand the systematic processes including algorithms that help in the storage, processing, communication, and access to information.
  • Powerful data sciences tools, including statistics and probability to perform technical analysis of data and help make informed decisions. 
  • R/Python – one of the most common programming languages among data scientists for a variety of data science projects and applications. It has a lot of features for dealing with data, statistics, building models etc.

Rahul Aggarwal said,“you need a lot of math and programming background just to even start.”

Qualifications and certifications

To give you an idea about what it takes to be an ML Engineer in big companies, let’s have a look at the eligibility criteria for an ML engineer at Amazon:

  • M.Sc in Computer Science or related field, or equivalent experience
  • Experience in machine learning/artificial intelligence, fairness, data quality, data science, or information integration research
  • Experience in general-purpose programming languages such as Scala, Python, Java, or C++

You can also assess the ability to frame ML problems, develop ML models, architect ML solutions, prepare and process data with a Professional ML Engineer exam from Google.

Wrapping up

Multiple tools are available for ML Engineers to work and learn, including:

  • Azure Machine Learning – a cloud platform from Microsoft to build, and deploy AI models. 
  • TensorFlow – an open-source platform from Google to develop and train ML models.
  • IBM Watson employs data to deploy machine learning and deep learning models.
  • OpenNN – an open-source neural networks library written in C++ programming language for machine learning. 

With IoTs turning mainstream with smart houses, smart wearables, smart cities etc, machine learning is here to stay. And it’s the future of jobs in more than one way.

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