Implementing MLOps practices with Amazon SageMaker
About this lesson
MLOps refers to a methodology that is built on applying DevOps practices to machine learning workloads. DevOps focuses on the intersection of development and operations disciplines to streamline software delivery across the Software Development Lifecycle(SDLC). MLOps focuses on the intersection of data science, data engineering in combination with existing DevOps practices to streamline model delivery across the Machine Learning Development Lifecycle (MLDC). In this session you'll learn best practices and how to address common challenges in this area for a successful MLOps adoption, implementation, and execution. Session delivered at the APN AI/ML Blackbelt track for AWS partners in Nov, 2021.
Original Description
MLOps refers to a methodology that is built on applying DevOps practices to machine learning workloads. DevOps focuses on the intersection of development and operations disciplines to streamline software delivery across the Software Development Lifecycle(SDLC). MLOps focuses on the intersection of data science, data engineering in combination with existing DevOps practices to streamline model delivery across the Machine Learning Development Lifecycle (MLDC).
In this session you'll learn best practices and how to address common challenges in this area for a successful MLOps adoption, implementation, and execution.
Session delivered at the APN AI/ML Blackbelt track for AWS partners in Nov, 2021.
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