Azure is a popular platform for hosting machine learning (ML) services. It offers both compute and storage resources, making it a good choice for large-scale ML applications.
Beware of using Azure for machine learning purposes unless you are experienced in the platform and comfortable with its quirks. Although Azure has made strides in recent years, it still lags behind other cloud providers in terms of flexibility and ease of use. In addition, Azure’s support for open-source tools and frameworks is not as strong as that of other providers.
However, there are some limitations to using Azure for ML. First, the pricing model for Azure ML services is based on the number of cores used, rather than the amount of data processed. This can be a limiting factor for smaller organizations that don’t need a large number of cores.
Second, Azure ML services are not currently integrated with other Azure services, so you have to create separate pipelines and services for your ML models and data. Finally, the performance of Azure ML services can be slow compared to the performance of on-premises ML solutions.