LLMOps for Speech Recognition with Whisper and OpenVINO

Deploying robust speech recognition systems requires more than just a good model; it demands effective operational practices. This course introduces you to the essential techniques for building, optimizing, and managing Automatic Speech Recognition (ASR) solutions using cutting-edge tools. By the end of this course, you will be able to design and implement ASR pipelines, leverage model optimization for performance, and apply LLMOps principles to ensure your speech-to-text applications are reliable and scalable. What you'll learn: * Understand the core concepts of Automatic Speech Recognition (ASR) and its practical applications. * Learn how the Whisper model functions for accurate speech-to-text transcription. * Apply OpenVINO for optimizing Whisper models for efficient inference on various hardware. * Grasp fundamental LLMOps principles for managing the lifecycle of ASR models. * Practice deploying and monitoring ASR solutions in a production-like environment. * Implement basic model versioning and update strategies for ASR systems. This course begins with foundational ASR terminology and concepts, then progresses to practical implementations with the Whisper model and optimization techniques using OpenVINO, concluding with LLMOps best practices for deployment and management. This course is perfect for beginners interested in machine learning operations, natural language processing, and deploying AI models. No prior experience with ASR or LLMOps is required. Begin your journey into building and managing state-of-the-art speech recognition systems today.