Video by H2O.ai via YouTube
Training a model is half the work. Getting it into production and keeping it reliable is where the real operational complexity lives.
Terms covered:
– MLOps: deployment, versioning, monitoring and retraining. What DevOps did for software engineering, MLOps does for machine learning.
– LLMOps: the same principles extended to language models, adding prompt versioning, token cost control, hallucination monitoring and context window limits
– Model Hub: a central repository where models are published, discovered, versioned and deployed from one place
Without a model hub, models scatter across teams and machines with no visibility into what is running where, or which version is live.
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AI Fundamentals Glossary | H2O.ai University
Full playlist: https://www.youtube.com/playlist?list=PLAWKlIKS-66E
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#MLOps #LLMOps #AIinProduction #AIGlossary #H2Oai