ML Orchestration, short for Machine Learning Orchestration, is the process of coordinating, managing, and integrating multiple machine learning models and tasks in an organized manner. It aims to streamline and automate the process of creating, deploying, and managing machine learning models. It includes various aspects such as model training, validation, deployment, monitoring, and versioning.
ML Orchestration in practice
In ML Orchestration, we first define the ML workflows that include the sequence of tasks executed in a machine learning project such as data gathering, preprocessing, model training, validation, and deployment.
Once workflows are defined, orchestration tools are used to automate these workflows. This involves setting up triggers for tasks based on certain events or conditions, managing dependencies between tasks, automatic resource allocation, parallel execution of tasks, error handling, etc.
With the help of ML orchestration tools, we can automatically retrain models on new data, update models with improved versions, monitor performance of models, and manage many models at once, which would otherwise be a daunting task.
In a nutshell, ML Orchestration involves setting up the whole machine learning process as a production-grade workflow, thereby enabling you to manage complex ML tasks more efficiently.
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