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Generative AI has surged in popularity in recent times. Over the past couple of months, every conversation I’ve had with data scientists and machine learning engineers invariably pivots to this fascinating domain.
As the public becomes increasingly attuned to its potential and the value it offers businesses, its adoption has accelerated. Software companies, ranging from startups to tech giants, are introducing their own GenAI solutions, guiding customers through this evolving landscape. Additionally, a growing number of technical experts, including data scientists, ML engineers, enterprise architects, and software developers, are experimenting with various concepts, underscoring the vast potential of generative AI.
With foundational models like OpenAI, Anthropic, Llama2, and Falcon40B combined with cutting-edge programming frameworks like Langchain, many have already witnessed early successes. Yet, a pivotal question arises when considering the production phase: How do we ensure it operates as intended and how can we monitor these generative AI use cases?
Monitoring AI and machine learning use cases is no novelty. Active oversight is vital to determine if the model behaves as intended, detect significant data drift, or evaluate potential deterioration in model performance from both technical and business perspectives. But generative AI presents unique challenges:
- Unstructured Data: Generative AI often deals with unstructured data like language, images, or videos, making quantification more challenging than structured data.
- Opaque Mechanisms: Unlike traditional ML models, where techniques like SHAP values can explain how inputs influence predictions, generative AI’s content creation mechanism is less observable.
- Model Integrity and Bias: Generative AI models can sometimes produce seemingly accurate yet misleading answers. They may also perpetuate stereotypes inherent in their training data or, in some cases, yield toxic responses.
- Prompt Sensitivity: Model responses are deeply influenced by their prompts, which are more subjective than traditional ML inputs.