Model optimization and monitoring techniques
Optimizing models for specific use cases is crucial. For traditional ML, fine-tuning pre-trained models or training from scratch are common strategies. GenAI introduces additional options, such as retrieval-augmented generation (RAG), which allows the use of private data to provide context and ultimately improve model outputs. Choosing between general-purpose and task-specific models also plays a critical role. Do you really need a general-purpose model or can you use a smaller model that is trained for your specific use case? General-purpose models are versatile but often less efficient than smaller, specialized models built for specific tasks.
Model monitoring also requires distinctly different approaches for generative AI and traditional models. Traditional models rely on well-defined metrics like accuracy, precision, and an F1 score, which are straightforward to evaluate. In contrast, generative AI models often involve metrics that are a bit more subjective, such as user engagement or relevance. Good metrics for genAI models are still lacking and it really comes down to the individual use case. Assessing a model is very complicated and can sometimes require additional support from business metrics to understand if the model is acting according to plan. In any scenario, businesses must design architectures that can be measured to make sure they deliver the desired output.
Advancements in ML engineering
Traditional machine learning has long relied on open source solutions, from open source architectures like LSTM (long short-term memory) and YOLO (you only look once), to open source libraries like XGBoost and Scikit-learn. These solutions have become the standards for most challenges thanks to being accessible and versatile. For genAI, however, commercial solutions like OpenAI’s GPT models and Google’s Gemini currently dominate due to high costs and intricate training complexities. Building these models from scratch means massive data requirements, intricate training, and significant costs.
