MLOps in Plain English: What It Is, What It Actually Looks Like, and Why Most Teams Get It Wrong
MLOps is gaining prominence as the critical discipline for deploying and maintaining machine learning models in production. While model training was once the primary focus, the operational aspects of MLOps are now considered more vital for real-world AI applications. This includes strategies for deployment, serving, and managing models, with specific attention to the unique challenges of Large Language Models (LLMs) compared to traditional ML models. Various tools and architectures, such as those utilizing Docker, Flask, AWS, and MLflow, are essential for building robust MLOps pipelines. AI
IMPACT Highlights the growing importance of operationalizing AI models, emphasizing the need for robust deployment and maintenance strategies.
