It is. The pricing details focus on VM compute because that's the largest percentage by far for most customers: azure.microsoft.com/en-us/pricing/details/machine-learning/ The longer form of what you're paying for: - Data stored in Azure Blob Storage and disk for the compute instances ($0.02 to $0.23 USD per GB) - VM compute for your compute instances and compute clusters *when they are running*. Stopped VMs don't incur charges, and this means more efficient code finishing jobs faster on smaller VMs leads to a smaller bill. - VM compute costs if you deploy the model using Azure Container Instances or Azure Kubernetes Service - Storage of images in Azure Container Registry. This is usually quite inexpensive. You can often get away with the Basic tier, which is $0.17 USD per day. Standard tier is $0.67 per day. - There are other fractional charges for Azure Key Vault, Application Insights, and Log Analytics. I don't even bother with Key Vault pricing as it's so little. App Insights and Log Analytics can get up there in price, but not for AML cases--the service just doesn't write enough to them to rack up an appreciable bill. - You might also incur networking fees if you are consistently sending large amounts of data into and out of Azure regions. I almost never think about this because it's typically such a low number, especially for ML scenarios. Aside from the core service costs, there's no additional fee to use Azure ML resources, e.g., some "number of trained models" or "number of API calls" fee. So the rule of thumb is, you're paying for VMs when they're running and data in your storage accounts, and that's going to be ~99% of your AML bill.
Great videos Can you confirm that Azure Machine Learning can be used by Data Engineers, Data Scientists, and AI/ML Engineers? Data Engineers bring the data into Dataset Assets, and Data Scientists and AI/ML Engineers use those datasets for analysis and model development in their VMs Azure Admin can assign the VMS to Data Scientists and AI/ML Engineers Can you confirm.
@satish1012 Yes, that's one way to do things. In Azure ML, data engineers can create the data assets and shape data. As far as VMs go, data scientists or ML engineers can use compute instances for training, or have their own data science VMs and run locally, though that's more of a code first solution. For low code solutions, compute instances are the norm.
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Studying for the Azure ML certification and appreciate this video, Kevin!
You got the content tha i was loking for lot of time ago, thanks a lot.
Thanks a lot for this video. It was exactly what I am looking for.
Amazing video dude! Thank You.
Thanks for sharing the content. I need those bulky clusters for my PhD experiments.
This is a great overview. Thanks 🎉
This s great overview ... Thank you
Thanks - great overview.
very nice video, thank you!
Great video! I wonder if Azure ML is charged by the processing time and VM configuration.
It is. The pricing details focus on VM compute because that's the largest percentage by far for most customers: azure.microsoft.com/en-us/pricing/details/machine-learning/
The longer form of what you're paying for:
- Data stored in Azure Blob Storage and disk for the compute instances ($0.02 to $0.23 USD per GB)
- VM compute for your compute instances and compute clusters *when they are running*. Stopped VMs don't incur charges, and this means more efficient code finishing jobs faster on smaller VMs leads to a smaller bill.
- VM compute costs if you deploy the model using Azure Container Instances or Azure Kubernetes Service
- Storage of images in Azure Container Registry. This is usually quite inexpensive. You can often get away with the Basic tier, which is $0.17 USD per day. Standard tier is $0.67 per day.
- There are other fractional charges for Azure Key Vault, Application Insights, and Log Analytics. I don't even bother with Key Vault pricing as it's so little. App Insights and Log Analytics can get up there in price, but not for AML cases--the service just doesn't write enough to them to rack up an appreciable bill.
- You might also incur networking fees if you are consistently sending large amounts of data into and out of Azure regions. I almost never think about this because it's typically such a low number, especially for ML scenarios.
Aside from the core service costs, there's no additional fee to use Azure ML resources, e.g., some "number of trained models" or "number of API calls" fee. So the rule of thumb is, you're paying for VMs when they're running and data in your storage accounts, and that's going to be ~99% of your AML bill.
Thank you very much for your great answer! I appreciated it!@@KevinFeasel
Great video.
Great overview
Great videos
Can you confirm that Azure Machine Learning can be used by Data Engineers, Data Scientists, and AI/ML Engineers?
Data Engineers bring the data into Dataset Assets, and Data Scientists and AI/ML Engineers use those datasets for analysis and model development in their VMs
Azure Admin can assign the VMS to Data Scientists and AI/ML Engineers
Can you confirm.
@satish1012 Yes, that's one way to do things. In Azure ML, data engineers can create the data assets and shape data. As far as VMs go, data scientists or ML engineers can use compute instances for training, or have their own data science VMs and run locally, though that's more of a code first solution. For low code solutions, compute instances are the norm.
Thanks big boss
Thanks
Could you please not use the background music? it's very distracting :(
No, it's perfect like this