Part 2/13:
Why Focus on Deployment?
Creating models is an important first step, but deploying these models at scale introduces unique constraints:
Cost of Serving Models: Running deep learning inference is resource-intensive, and inefficient deployment can lead to skyrocketing expenses.
Total Cost of Ownership (TCO): Implementing optimized serving platforms reduces operational costs, thereby lowering the TCO and making AI solutions more accessible for organizations.
Multi-Model and Multi-Modal Ecosystems: Future AI applications won't rely on a single model or modality. Combining computer vision, NLP, speech synthesis, and recognition in a seamless pipeline requires robust, versatile deployment solutions.