As per a report, the global machine learning market will reach Rs 543 billion valuation by 2023.

Machine learning (ML) has been a very disruptive force, pivotal in introduction the fourth technological revolution . Forward-looking retailers, automotive players, financial services firms, game developers, researchers, etc. have taken to the present AI technology sort of a duck to water. As per a report, the worldwide ML market will reach Rs 543 billion valuation by 2023.

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.

Machine learning structures our industries that believe large amounts of knowledge and wish a framework to analyse it to optimise outcomes. AWS uses ML For detecting abnormal machine behaviour, Disney for animation, researchers for locating new planets; the utilization cases are plenty.

Starting a career in machine learning

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

Research Scientist and Research Engineer are popular variants of machine learning engineers. To have a career in machine learning , you 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:

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• 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.

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.

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.