Machine Learning Engineers – Skill Gaps in Australia

Global Employability

Digital technology can be said to be driving our collective growth and advancements. No matter which field we work in, technology seems to be at the forefront. At present, artificial intelligence (AI) and machine learning is at the core of most digital technologies today across a variety of fields and applications. As such, the demand for those with relevant skills in the areas is welcome in most parts of the world – including Australia! But the number of engineers that are available on hand is too low compared to the demand. Why? Lack of required skills.

Digital Australia - Or Is It?

In the past couple of years, we have seen the tide turn in favour of more robust remote digital technologies that have been enabling people all over the world to work from their homes. Scientific development in various fields, especially in extended reality, remote technologies, cloud computing, machine learning, and autonomous vehicles have increased manifold and most of these trends are sure to stay on in a post-COVID world.

Australia is looking to improve its digital technology research and development areas currently. In fact, the Australian Academy of Science and the Australian Academy of Technology’s new joint report addresses the digital gap that Australia faces and the necessary steps it needs to take in order to embrace the digital future. Alex Haloulos, director of Sage Software opines that Australian organisations are being held back by the “lack of highly skilled workers”. In one of the latest reports, it was estimated that Australia would need close to 6.5 to 7 million (yes, million!) digital workers in the next four years (by 2025) to address its digital technology gap. To address this demand, Australia has begun encouraging the growth digital technologies such as AI, machine learning, cloud computing, the Internet of Things, extended reality, blockchain, 5G networks, smart microgrids, and quantum computing.

In Australia, the top digital skills that are in demand currently include cloud computing, technical operations/support, cyber security specialisation, web development, game development, AI-based development skills, machine learning expertise, and data science expertise. Out of these, machine learning is a term that is most frequently used but in reality, very few people truly understand its capabilities.

Machine Learning Skill Gaps in Australia

  • STEM background, Statistical and Analytical Skills – In order to build efficient machine learning algorithms, it is important to understand how they work from a mathematical standpoint. In fact, most algorithms are designed using mathematical and statistical formulae and methods. Analytical skills are critical to understand what a collection of data means or describes. Having a background in science, technology, engineering or mathematics is critical to becoming an ML engineer.
  • Programming Skills – To become a good ML engineer, one needs to understand how to program first. By learning how to program in different languages including Python, R, C++, C, Java, Scala, and Go, one can efficiently implement ML models to address various real-world problems. One must also be familiar with environments such as Matlab, Hadoop, and Spark.
  • Data science skills – ML requires data in large amounts and this data requires a lot of modelling or pre-processing before it can be used for analytical or training purposes. Therefore, understanding how to handle large amounts of data (data modelling) is an important skill.
  • Problem Solving and Critical Thinking – ML methods require a fair amount of critical thinking in order to figure out what ML model is to be applied to the kind of problem at hand. Since every data set that passes through these models is unique and since every output varies based on how we set the parameters, it is pretty important to possess critical thinking capabilities.
  • Understanding ML-related technologies – This includes various ML workflows and models, simulation tools, learning to use technologies such as TensorFlow, knowing about the different packages used for various purposes, natural language processing, robotic process automation.
  • Communication Skills – Great communication skills is the hallmark of a good ML engineer because one will be working with a lot of non-technical individuals. Also, it isn’t always about what data conveys but how you put it to good use.

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Global Employability


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