The Generative AI Life-cycle

The common AI/ML Lifecycle consists of data collection, preparation, training, evaluation, deployment and monitoring all encompassed with an MLOps pipeline. Generative AI (GenAI) is a transformational technology that will continue its ramifications of major industry shifts in the coming months and years. Currently, in its earlier stages it has a raised a lot of hype; a distraction to fundamental shift that underlies its promise. The use of Generative AI in the Enterprise in a reproducible, scalable and responsible manner that minimizes risks of AI Safety, Misuse Mitigation, and Model Robustness calls for an augmentation of the common ML lifecycle. In this article, we will explore the initial set of nuances and augmentations needed to adopt generative AI in the enterprise. Let’s start by contrasting a couple of terms in the AI/ML Spectrum. <h1>Predictive AI and Generative AI</h1> Generative AI and predictive AI are two different types of artificial intelligence (AI) that are used for different purposes. Generative AI is used to create new content, such as music, images, and texts, while predictive AI is used for clustering, classification and regression that often relies on supervised learning and historical training sets that create models for predictions about future states or events. For example, a predictive AI model that is trained on a dataset of historical data about the stock market may be able to make predictions about the future prices of stocks. Generative AI models are built by training on a large dataset of general examples (such as wikipedia, commoncrawl, etc) and then using that knowledge to generate new examples that are similar to the training data. So the idea is to generate new data. A generative AI model that is trained on a dataset of images of cats may be able to generate new images of cats that are similar to the training data. Generative AI models are often used in creative applications, such as creating new works of art or music. Predictive AI models are often used in business applications, such as predicting customer behavior or making financial decisions. <a href="https://dr-arsanjani.medium.com/the-generative-ai-life-cycle-fb2271a70349">Website</a>