MLOps framework for Generative AI
1. MLOps framework for Generative AI
Now that you're familiar with the traditional or predictive ML workflow, let's focus on how MLOps can be specifically tailored for generative AI applications. MLOps for Gen AI can be defined as a set of practices, techniques and technologies that extend MLOps principles to the operationaliization of generative AI applications. While the foundation of MLOps remains essential, generative AI introduces unique considerations that require additional layers and refinements to the traditional workflow. Let's start with the experimentation phase. This phase is distinct in two ways. Pre-trained model discovery, where the emphasis shifts from building models from scratch to discovering and leveraging Pre-trained models and the direct prediction shortcut, where a direct path from the discovery and experimentation phase enables immediate prediction execution using natural language prompts. After the discovery and experimentation phase, we proceed with training and serving our models as we would in any ML workflow. However, with generative AI, we introduced two new phases customization and tuning and curated data. Customization and tuning instead of training models from scratch, generative AI focuses on customizing and fine tuning pre-trained models for specific tasks. This approach allows us to leverage the power of Pre-trained models while tailoring them to our specific needs. Curated data: Generative AI relies on curated datasets rather than massive amounts of labeled data. This necessitates a reevaluation of data lake and data warehouse strategies to ensure that we have the right data available for training and serving our models. The other necessary adaptation of MLOps for Gen AI is in the governing artifacts phase. Generative AI introduces additional artifacts such as tuning jobs, adaptive layers, and embeddings, that require governance. The next adaptation is in the evaluating and monitoring phases. Traditional metrics like accuracy and precision might not suffice for evaluating generative AI models. New metrics that consider fluency, factuality and brand reputation are often required. Check the documentation for further details of new evaluation metrics and services. Vertex AI offers. In the final phase curate data or incorporating enterprise data phase, it appears that foundation models are somewhat static, confined the knowledge they acquired during pre-training. The true potential lies in accessing data beyond their original training sets, allowing them to perform more sophisticated tasks. However, integrating additional data introduces new evaluation and monitoring challenges, increasing the overall complexity. With these insights in hand, let's move on to explore the practical tools and strategies Vertex AI offers to streamline your generative AI MLOps journey.2. Let's practice!
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