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My question is, what is the better location for me to deploy the pipeline, using Azure functions or Azure Machine Learning (or any other Azure tool)? I am aware of Azure functions and it’s flexibility’s in running my scripts and all the triggers, however I am not so familiar with Azure machine learning, what are its advantages and disadvantages over using functions?
Thanks for your interest in Azure.
Please check the below information for more details on both Azure Machine Learning and Azure Functions.
Azure Functions and Azure Machine Learning are good options for deploying your ML pipeline. However, they have different use cases and advantages.
Azure Machine Learning is a cloud-based service that provides a complete environment for building, training, and deploying machine learning models. It provides a range of tools and services to help you manage the entire lifecycle of your ML models. Azure Machine Learning provides a lot of flexibility in terms of the types of models you can build and deploy, and it supports a variety of programming languages including Python.
I would suggest you, check the below documentations for more details.
Azure Functions is a serverless compute service that allows you to run your code on-demand without having to manage any infrastructure. It is a great option if you have a small script or function that needs to be executed in response to an event or trigger. Azure Functions can be triggered by a variety of events such as HTTP requests, timers, and message queues. It also supports a variety of programming languages including Python, which is great for ML pipelines.
I am aware of Azure functions and it’s flexibility’s in running my scripts and all the triggers, however I am not so familiar with Azure machine learning, what are its advantages and disadvantages over using functions?
One of the main advantages of Azure Machine Learning over Azure Functions is that it provides a complete environment for building and deploying ML models. This includes tools for data preparation, model training, and model deployment. Azure Machine Learning also provides a range of deployment options including Azure Kubernetes Service (AKS), Azure Container Instances (ACI), and Azure Functions.
To decide which option is best for you, you need to consider your specific requirements. If you have a small script or function that needs to be executed in response to an event or trigger, Azure Functions is a great option. However, if you need a complete environment for building, training, and deploying ML models, Azure Machine Learning is the better choice.
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