From the course: MLOps and Data Pipeline Orchestration for AI Systems

Importance of MLOps

From the course: MLOps and Data Pipeline Orchestration for AI Systems

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Importance of MLOps

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- [Narrator] Hi, and welcome to this course on MLOps and Data Pipeline Orchestration for AI Systems. First, let's talk about the importance of MLOps, or ML operations, and where LLMOps fit in. MLOps or machine learning operations help operationalize and scale ML workflows. They enable consistent, repeatable, and scalable deployment of machine learning models and AI systems across environments, whether it's dev, test, or prod. It provides mechanisms for versioning, validation, testing, and monitoring models in production to detect drift and performance degradation. This ensures model quality and reliability. MLOps integrates data science, engineering, and DevOps teams through pipelines that automate the training, deployment, and update of models. This improves collaboration between your teams and the automation of your systems. And finally, MLOps ensures that all aspects of the machine learning lifecycle, whether it's data, code, or models, are logged and auditable, and this supports regulatory and business requirements. Large language models, or LLMs, are usually generative AI models that are trained on a huge corpus of data and have billions of parameters. LLMOps helps orchestrate fine tuning, prompt engineering, and model deployment of LLMs at scale. These tend to be far more complex than traditional ML models. LLMOps is all about monitoring and optimization of resource usage such as GPUs, graphics processing units, or TPUs, tensor processing units. They help monitor and control model size and inference time to keep LLM applications cost-effective and responsive. LLMOps supports auditing, moderation, and prompt injection defenses to reduce the risks of harmful or biased outputs in production. And finally, it facilitates automated testing, human in the loop feedback and performance tracking to iteratively improve LLM responses over time. This is continuous evaluation and feedback.

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