Machine learning is a branch of AI in which systems learn patterns from data to make predictions or decisions. The behaviour comes from training on examples rather than from rules written by hand.
A machine learning engineer builds, deploys and maintains machine learning models as reliable software in production. Teams usually hire one once a model has proved its value and must run, scale and stay accurate every day.
Artificial intelligence (AI) is the field of building computer systems that can perform tasks that normally require human intelligence. These include understanding language, recognising images, making predictions and deciding what to do next.
Data labeling is adding correct answers, such as categories, tags, boxes or ratings, to raw examples. The labelled data is then used to train or evaluate a machine learning model.
Data science is the discipline of extracting insight and predictions from data by combining statistics, programming and domain knowledge. It turns raw records into answers, forecasts and models that guide decisions.
A data scientist uses statistics, programming and domain knowledge to find patterns in data, test ideas and build predictive models. Hiring one pays off when a team has reliable data and decisions that better evidence can improve.
Deep learning is a type of machine learning that uses neural networks with many layers to learn complex patterns. It excels with large amounts of unstructured data such as images, audio and text.
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