AI Algorithms Developer Workbench
The result of an AI engineer’s work is a neural network (a computer program). The specialist ensures that it learns to recognize the data loaded into it, identify patterns, make predictions, and make decisions independently.
AI models are in demand in many fields, so the list of a specialist’s responsibilities will depend on the employer’s goals. However, a number of typical tasks can be identified that are common to most projects:
• research of the field for which the machine learning model is being constructed, and selection of suitable algorithms; • assessment of potential risks of using a neural network in a target area; • collecting data for the formation of an AI training set, visualizing them and checking for possible patterns; • Model design, architecture work; • neural network programming; • application of machine learning algorithms to the model; • neural network training using previously collected training data; • testing the resulting model and correcting detected errors; • software development for maintaining a ready-made neural network; • implementation in business processes (deployment), monitoring and maintenance of the network model (refinement, scaling, bug fixing, anti-retraining); • converting the ML model into an API (application programming interface) for other applications to use its capabilities; • Interaction with the analytics and technical support teams throughout the life cycle of the program.
