Month: December 2021

ADO and terraform for IAC

Azure Devops pipeline in combination with Terraform can be used to deploy resources in Azure . ADO can be deployed on prem , but the better option is to use the cloud version that is found at dev.azure.com

Ado has a a build pipeline and a release pipeline. The build pipeline is used to build artifacts ( Continuous Integration ) and the Release pipeline is used to deploy these artifacts to higher environments.

In the case of terraform , we are actually building the environments , so the release pipeline does not really apply here , we can pretty much do our terraform stuff from our build pipeline .

We can always run terraform from our local desktop , but that just doesn’t scale well for larger teams and organization.

The better approach would be to structure our infrastructure builds in a highly templatized form , meaning everything would be captured in variables. At a high level this would mean create a shared repo where we define terraform modules. The terraform module would encompass multiple resource definitions.

The deployment would essentially be pulling the appropriate modules and the populating the variables ike subscription id , resource group for your specific project. Overall the iac project would look like this

Loss Function

its one of the basic terms you will come across in machine learning . This is how the model gets optimized and sort of directs itself to the correct solution. When the expected output differs from the actual output , you have difference and you can program the model training to adjust itself to reduce this error in the next iteration. This is where loss function comes to play. Different loss functions will perform better with different problem and this is where its important to pick the right kind of loss function. For regression kind of problem , mean square error is a better fit whereas for classification problems we go with cross entropy ( log fn ) is a better fit .