Sure! Here’s the translation into American English:
Organizations that embark on evaluating generative artificial intelligence models often make decisions based on three main dimensions: accuracy, latency, and cost. While these criteria are a good starting point, they may overly simplify the complexity of the factors impacting a model’s performance. Foundation models are revolutionizing the development of generative AI applications, offering unprecedented capabilities to create and understand content that resembles human-generated material. However, as the number of available models expands, selecting the most suitable one becomes a significant challenge for organizations.
Amazon Bedrock, a managed service, provides a wide array of high-quality models from leading AI companies through a single API. Although this flexibility is advantageous, it brings about an important dilemma: which model will optimize performance for a specific application while also meeting existing operational constraints?
Client studies have revealed that in many initial generative AI projects, model selection is based on limited testing or the provider’s reputation rather than following a systematic approach aligned with business requirements. This often results in computational resource overload, performance that falls short of expectations due to a misalignment between model strengths and use case needs, and high operational costs from inefficient token usage.
To address these issues, a comprehensive evaluation methodology has been proposed, optimized for implementations on Amazon Bedrock. This methodology merges theoretical frameworks with practical strategies that enable data scientists and machine learning engineers to make more informed decisions about model selection.
Model performance is assessed using a multidimensional framework that considers various critical factors, such as effectiveness in specific tasks, architectural features, operational considerations, and attributes of responsible AI. The methodology suggests a four-phase approach: requirements engineering, candidate model selection, systematic performance evaluation, and decision analysis.
As organizations progress in their AI efforts, it’s essential to consider changing needs and technological advancements. Thus, model selection should be viewed as a dynamic process that evolves in line with new developments in the field of artificial intelligence.
Source: MiMub in Spanish
