AI Solutions LifeCycle

The AI lifecycle is a structured process encompassing the key stages required for the development, deployment, and ongoing management of an artificial intelligence (AI) system. Adhering to this lifecycle allows organizations to optimize model performance, make informed, data-driven decisions, and effectively respond to evolving needs. The AI lifecycle includes the following phases: Problem Identification, Data Collection and Preparation, Model Selection, Model Training, Model Assessment, and Model Deployment.

At the UMass Chan AI Assurance Lab, our focus will be on the Model Assessment and Evaluation phase. Our services are designed to help organizations ensure that their AI models are accurate, reliable, and capable of performing well in real-world scenarios. Model assessment and evaluation are critical stages in the AI lifecycle, as they validate that the model not only performs well on training data but also generalizes to new, unseen data. This process is essential for identifying and addressing issues such as overfitting, underfitting, and model bias. Comprehensive evaluation and validation are integral to building robust, trustworthy AI systems that maintain high performance across varied data environments and use cases.