AI development is a multistage process of creating, deploying and maintaining artificial intelligence (AI) models that drive business value. The AI development lifecycle consists of three key phases: Gather Requirements, Assess Feasibility and Implement Security & Compliance. Each stage plays a critical role in addressing specific challenges and ensuring the AI solution meets desired objectives.
Many modern products are enhanced by AI capabilities, including automation, conversational platforms and bots, smart machines and home and office automation systems. AI can perform repetitive tasks, like verifying documents or transcribing customer phone calls, freeing human capital to work on more impactful projects. AI can also automate more dangerous or complex work, such as diagnosing patients in the field or driving vehicles.
The next phase in AI development is training the model on data to learn patterns and relationships, and develop a level of skill that allows it to make predictions or decisions. This is an iterative process that adjusts internal parameters to minimize errors over time. The quality of the training is directly related to the accuracy and performance of the model.
The final phase of AI development is deploying and monitoring the model. AI systems are vulnerable to operational risks such as model drift and bias that can have serious consequences for the organization, such as privacy violations, data breaches or cybersecurity attacks from threat actors. Monitoring and implementing security and compliance at each step of the AI development lifecycle helps ensure the integrity and availability of the model.
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