From the course: Amazon SageMaker for Generative AI Applications
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Understand MLOps - Amazon SageMaker Tutorial
From the course: Amazon SageMaker for Generative AI Applications
Understand MLOps
- [Instructor] You've trained the model, deployed the model, but now what? In a real-world environment, your job isn't done once the model goes live. That's where MLOps comes in. It's how you manage machine learning at scale with repeatability, reliability, and governance. MLOps, short for Machine Learning Operations, is the practice of applying DevOps principles to the machine learning lifecycle. It's all about putting structure around how models are built, deployed, monitored, and improved over time. Think of it as the glue between your data science experiments and your production systems. Without it, models become hard to maintain, difficult to scale, and almost impossible to audit or govern. There are a few key components that make up a solid MLOps pipeline. Model training pipelines. Automating the steps needed to train and retrain your model with updated data. Model versioning. Keeping track of every version of your model, your data, and your code. Deployment automation. Pushing…
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