What Does a Machine Learning Engineer Do?

For data analysts exploring new opportunities and seeking ways to move up the career ladder, one option is to become a machine learning engineer. A role high in demand and short in supply, ML engineers are vital not just to the data science industry, but to any organization that places data at the heart of its strategy. But what exactly does a machine learning engineer do, and which kinds of skills are best suited to this position? In this article, we'll explore everything you need to know about machine learning engineering. We'll also explore some starting steps for those interested in pursuing a career in this field. We'll cover the following topics: What is machine learning? What do machine learning engineers do? Is there demand for machine learning engineers? How to become a machine learning engineer Next steps Ready to expand your knowledge of machine learning engineering? Let's kick off with the basics. 1. What is machine learning? Machine learning is a branch of artificial intelligence (AI) that enables computers to learn from data without being explicitly programmed to do so. Using algorithms, machine learning involves detecting patterns in data, allowing computers to make predictions—and, in many cases, decisions—without human intervention. Machine learning tools essentially allow computers to 'think' and 'learn' autonomously. Learn more in our full guide to machine learning. Machine learning was initially conceived in the 1940s, with the first executable algorithms developed throughout the 1950s and 60s. However, only with advances in technology and computer processing power has it entered its heyday. While the first machine learning algorithms were developed for the sciences, it is now an integral part of many industries, from healthcare to retail. It is used to automate complex tasks, provide insights, and drive better decision-making. Contemporary examples of machine learning in action include: Automating customer service tasks, such as responding to inquiries or providing personalized recommendations Offering hyper-personalized marketing based on consumer interests and past behaviors Optimizing and managing supply chains by predicting customer demand and ensuring stock availability Improving medical diagnoses by analyzing medical images to diagnose diseases more quickly and accurately than using manual methods alone Supporting self-driving cars by using algorithms that detect objects in the environment and make navigation decisions Utilizing algorithms for facial recognition to improve security measures The list, as you can imagine, goes on! 2. What do machine learning engineers do? Machine learning engineers are responsible for developing and refining the algorithms utilized by machine learning tools. As a high-level role, it is their job to work with fellow data scientists and professional stakeholders to devise solutions to various problems. Typically machine learning applications might include: Natural language processing (for identifying customer sentiments, for example) Image recognition (such as that commonly used in policing or security) Machine vision (a subset of image recognition that allows computers to extract information from visual images) Speech recognition (for example, personal voice assistants) Financial modeling (for predicting stock prices or forecasting economic trends) Biomedical applications (such as discovering new drugs) Fraud detection (through monitoring of debit or credit card transactions) Recommendation engines (such as those used by Netflix or Amazon) Once again, the list could go on! The main thing to understand is that engineers and analysts use machine learning to automate tasks that are highly complex, time-consuming, and difficult for humans to complete accurately on their own. However, while these are the clear benefits of machine learning, the trade-off is that ML algorithms need to be custom-designed and developed to meet a particular demand. This differs from most traditional data analytics algorithms, which tend to be more general-purpose and require—if not zero fine-tuning—then much less additional input.

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