Professionals who are finding how to become an AI engineer should also know about the skills required in this field. Some of them include:
The first skill required to become an AI engineer is programming. To become well-versed in AI, it’s crucial to learn programming languages, such as Python, R, Java, and C++ to build and implement models.
To understand and implement different AI models—such as Hidden Markov models, Naive Bayes, Gaussian mixture models, and linear discriminant analysis—you must have detailed knowledge of linear algebra, probability, and statistics.
AI engineers work with large volumes of data, which could be streaming or real-time production-level data in terabytes or petabytes. For such data, these engineers need to know about Spark and other big data technologies to make sense of it. Along with Apache Spark, one can also use other big data technologies, such as Hadoop, Cassandra, and MongoDB.
Understanding how machine learning algorithms like linear regression, KNN, Naive Bayes, Support Vector Machine, and others work will help you implement machine learning models with ease. Additionally, to build AI models with unstructured data, you should understand deep learning algorithms (like a convolutional neural network, recurrent neural network, and generative adversarial network) and implement them using a framework. Some of the frameworks used in artificial intelligence are PyTorch, Theano, TensorFlow, and Caffe.
AI engineers need to communicate correctly to pitch their products and ideas to stakeholders. They should also have excellent problem-solving skills to resolve obstacles for decision making and drawing helpful business insights.
Let us explore the career and roles in AI in the next section of the How to become an AI Engineer article.
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