👉 Boost engineering is a critical subset of software engineering focused on enhancing and optimizing the performance, scalability, and reliability of machine learning models and systems. It involves applying advanced techniques such as hyperparameter tuning, model pruning, quantization, and knowledge distillation to improve efficiency without sacrificing accuracy. By leveraging these methods, engineers can reduce computational costs, accelerate inference times, and deploy models on resource-constrained devices. Boost engineering also includes monitoring and adapting models in production to handle changing data distributions, ensuring that the systems remain robust and effective over time. This holistic approach ensures that machine learning solutions are not only accurate but also practical and cost-efficient in real-world applications.
