Today we will be evaluating the difference between of Azure Machine Learning Services and Azure Machine Learning Studio that will help us to choose the best option to develop our Machine Learning solution.
First lets start with Azure Machine Learning Studio which offers a robust set of tools designed to develop, deploy and manage machine learning projects.
Azure Machine Learning Studio
- Azure Machine Learning Studio is a collaborative, drag-and-drop environment where you have No Coding is required and It’s a tool where you can use to build, test, and deploy predictive analytics solutions on your data.
- Uses pre-build and pre-configured machine learning algorithm and data handling modules as well as proprietary compute platform.
- Azure Machine Learning Studio does not have much control on training scalability.
- It has standard experiments but you can get quick results in lesser time.
Second, we have is Azure Machine Learning Services which you can find in two flavors, a Python SDK and a drag-and-drop style Visual Interface(preview).
Azure Machine Learning Service
- Azure Machine Learning Service provides both SDKs (Code based) and visual interface to quickly prepare data, train and deploy machine learning models.
- We can have the same drag-and drop experience to Machine Learning Studio, however, unlike proprietary compute platform of studio, the Visual Interface uses your own compute resources.
- It integrates with python environment, frameworks and tools and it does not restrict you what you need to use.
- Azure Machine Learning Service have full control on training scalability with custom compute targets.
Now, as we have got the idea about both Azure Machine Learning Studio and Azure Machine Learning Service, we can assume that from being a developer Azure Machine Learning Service is more flexible compare to Azure Machine Learning Studio.
I hope this short post has helped you get a better idea about Microsoft Azure Machine Learning offering products. If you have any questions, please feel free to contact me [email protected]