The position of machine learning engineer describes an engineer working with one of the various machine learning models. A machine learning engineer develops the infrastructure to support the model. This may be on a large or small scale depending on the company and the scope of work. To work in the realm of artificial intelligence (AI), a machine learning engineer should have a solid background in data science, applied research and coding. He/she may use a wide range of programming languages and technologies such as C++, Java, Python, Hadoop, Spark and Scala. A machine learning engineer essentially runs the operations of the machine learning project. In addition to in-depth knowledge of programming languages, a machine learning-engineer should also have strong mathematical skills, cloud application knowledge and have the ability to effectively communicate with other team members.
Technically, machine learning engineers are computer programmers, but their skills go way beyond programming machines to do specific tasks. The machine learning engineer's goal is to develop programs that enable machines to take actions without specific directions to do so. For example, programming a self-driving car is a type of project that a machine learning engineer might work on. Machine learning engineers generally use three main types of machine learning algorithms in their projects. These include Supervised Learning, Unsupervised Learning and Reinforcement Learning. Within each of these three categories, there are various specific algorithms that machine learning engineers can work with. Artificial intelligence is the wave of the future. In the US alone, it was estimated to be a $2.42 billion industry for 2017. By 2025, this industry is expected to grow expenentially.
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