From the course: Learning Amazon SageMaker AI
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Operationalizing with MLOps - Amazon SageMaker Tutorial
From the course: Learning Amazon SageMaker AI
Operationalizing with MLOps
- [Instructor] Training and deploying your model is a significant milestone, but the work doesn't end there. To keep your model running smoothly in a real-world environment, you have to make sure it's properly maintained, monitored, and integrated into existing workflows. This is where MLOps, machine learning operations, comes into play. It combines machine learning with operations to streamline and automate the entire lifecycle of your model. MLOps is a set of practices that aim to deploy, manage, monitor, and maintain machine learning models in production. It combines principles from DevOps for software development with the unique challenges of machine learning, such as handling data drift, retraining models, and tracking model performance. Data changes, environment shift, and models can degrade. MLOps practices help you stay on top of these challenges and maintain a smooth workflow for keeping your model up to date. SageMaker Pipelines allows you to implement MLOps. It helps you…
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