I am trying to integrate MS Dynamics Customer Insights (CI) with the model I have built within the new Azure Machine Learning (designer). Currently, I see there is only an integration between CI and Azure Machine Learning studio (classic).
I have deployed my model behind a web service (REST) within new Azure Machine Learning however it is not getting picked up in CI. However, I am able to score/generate predictions from the API using a Python script.
Please recommend a way to integrate these two MS services or suggest an architecture where CI can pick up the results.
Here are some hands-on labs which walk through the complete lifecycle for integrating Dynamics 365 Customer Insights with Azure data services. One scenario seen in Lab 5 is integrating the results of an Azure Machine Learning model back into Customer Insights. That walkthrough should give you one good way of accomplishing that task.
I totally agree that there's a real opportunity for a more seamless integration between Azure ML and D365 (including CI). For me, I'm thinking about how cool would it be to have an ML Dataset be automatically created with D365 data that could be used in the ML designer. After which the winning model could be registered, then made available inside D365 as a field that would score things in real time.
If you an "idea" on the forum below. My team and I will upvote it!
https://experience.dynamics.com/ideas/
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I have several Power Platform components, such as a Power App and some Power Automate flows that are part of a larger application. This application also consists of a Python package, a SQL database and so on.
I am trying to deploy this application on a client's environment. Now, in terms of the Python package and SQL database, for instance, this would mean deploying it into their Azure subscription or tenant.
How would this work for the Power Platform components? How can I deploy it on their subscription?
Note: I am not sure if subscription is the correct term to use here. As far as I know, Power Platform is not a part of Azure, but it comes under an O365 or D365 subscription?
I know Power Apps and flows can be exported and imported into other environments/subscriptions. But, is there some way I can maybe set up a CI/CD solution in order to achieve this? Maybe using Azure DevOps?
I have come across this, but from what I understand, I believe this is about deploying these components in different environments within the same subscription?,
https://learn.microsoft.com/en-us/azure/architecture/solution-ideas/articles/azure-devops-continuous-integration-for-power-platform
When you use Azure as part of you Power Platform solution, the right term is "tenant". 1 tenant can have multiple power platform environments and azure subscriptions.
https://learn.microsoft.com/en-us/power-platform/admin/environments-overview#environment-scope
Devops for Power Platform is simpler. Best way to authenticate is using service principal:
https://learn.microsoft.com/en-us/power-platform/alm/devops-build-tools#connection-to-environments
The Azure part is more complicated. if you want to fully automate you will need to write some infrastructure as code to create resources like an azure function or a database and after that the pipeline to deploy the code to those resources.
I am new to Azure ML. I am having some doubts .Could anyone please clarify my doubts listed below.
What is the difference between Azure ML service Azure ML experimentation service.
What is the difference between Azure ML workbench and Azure ML Studio.
I want to use azure ML Experimentation service for building few models and creating web API's. Is it possible to do the same with ML studio.
And also ML Experimentation service requires me to have a docker for windows installed for creating web services.
Can i create web services without using docker?
I'll do my best to answer these questions and feel free to ask more questions. :)
What is the difference between Azure ML service Azure ML experimentation service?
Essentially, Azure ML Service (I may reference this as Azure ML Studio) uses a drag and drop interface to build out your workflow and test models. Azure ML experimentation is a new offering from the Azure Portal to host them directly in Azure and offer a better way to manage your models. Experimentation will use Azure ML Workbench to build out your models.
What is the difference between Azure ML workbench and Azure ML Studio?
The biggest difference is ML Studio has the drag and drop interface to build the workflow and models, whereas Workbench lets you use Python to programmatically build out your models. Workbench also includes a really nice and powerful way to clean your data from the app. In Studio you have some good modules to clean data, but I don't think it's as powerful as what you can do in Workbench.
EDIT: The Workbench application is deprecated and has been replaced by/upgraded to ML Services. The core functionality is unchanged, though.
I want to use azure ML Experimentation service for building few models and creating web API's. Is it possible to do the same with ML studio?
I would actually say it's much easier to do this in ML Studio. The drag and drop interface is very intuitive and it is only a couple of clicks to create a web API to call your model. I feel, as it is currently at the time of this writing, is more complex to deploy your model and it involves using the Azure CLI.
And also ML Experimentation service requires me to have a docker for windows installed for creating web services. Can I create web services without using docker?
Here I'm not too familiar with the Docker parts of Workbench, but I believe you can create and deploy without using Docker. It will require an Azure Model Management resource, though, I believe.
I hope this helps and, again, feel free to ask more questions.
The AML Experimentation is one of our many new ML offerings, including data preparation, experimentation, model management, and operationalization. Workbench is a PREVIEW product that provides a GUI for some of these services. But it is just a installer/wrapper for the CLI that is needed to run. The services are Spark and Python based. Other Python frameworks will work, and you can get a little hacky to call Java/Scala from Python. Not really sure what you mean by an "Azure ML Service", perhaps you are referring to the operationalization service I mentioned above. This will quickly let you create new Python based APIs using Docker containers, and will connect with the model management account to keep track of the linage between your models and your services. All services here are still in preview and may breaking change before GA release.
Azure ML Studio is an older product that is perhaps simpler for some(myself an engineer not a data scientist). It offers a drag and drop experience, but is limited in it's data size to about 10G. This product is GA.
It is, but you need smaller data sizes, and the job flow is not spark based. I use this to do rapid PoC's. Also you will less control over the scalability of your scoring (batch or real time), because it is PaaS, compared to the newer service which is more IaaS. I would recommend looking at the new service instead of studio for most use cases.
The web services are completely based on Docker. Needing docker for experimentation is more about running things locally, which I myself rarely do. But, for the real time service, everything you package is placed into a docker container so it can be deployed to an ACS cluster.
Recently I have become interested in creating a Microsoft Azure Marketplace plugin for our API Developer Portal product DynamicApis.com. We currently have a BizSpark account but I am having trouble finding out where to start when it comes to creating and uploading a Marketplace application on Azure. Here are some questions that I have.
What is the process that I need to go through in order to apply to put my product on Azure Marketplace?
I am assuming there is an API that Azure has that you use to plug-in your application. Where can I find information on that API, sandbox environment, etc?
Is there any helpful documentation, contacts, or any material that anyone can provide me to help with this process
To get started with publishing in the Azure marketplace, follow this getting started guide.
I am starting my journey of IoT development with MS Azure. I would like some insight on the Azure cloud. I am a total newbie on cloud development. Can someone tell me some good books/links on Azure that will help me understand how I can use Azure for IoT and start development on the same.?
Thanks a lot for your inputs.
This totally depends on the architecture of your application. You can use SAAS components for rapid prototyping, parts or all of your application architecture. This will give you a better insight into selecting the appropriate stack of tools for your application.
If you want to deploy your own software stack, you would provision Azure Virtual Machines. Azure provides an SDK to interact with the cloud infrastructure.
Docker is a really good option to use for application deployment these days. Google provides better support for Docker containers using its Kubernetes framework.
Simple APIs or website can be developed on azure using Azure webapps. I am currently developing a node application using azure websites. The actual container where the site runs is a windows NT machine with IIS. If you want your SAAS server container's to be linux based then you might look at AWS/Google or Redhat Openshift.
I have used OpenShift SAAS, and found it quite easy to get onboard with.
I advise you to have a look at Build and Ignite events, this week. There might be more announcements there. You can definitely have a look at the following white paper: http://download.microsoft.com/download/E/1/F/E1FFDADF-C0FF-4E72-A834-B173A079F393/Microsoft_Internet_of_Things_White_Paper.pdf
The most important services for IoT in Azure are (until today):
Azure Event Hubs: a massive ingestion service that can take in millions of telemetry events per second.
Azure Stream Analytics: Real time complex event processing, combining multiple incoming streams of data and detection patterns in it
PowerBI: this will allow users to build and explore interactive reports and graphs
Azure Machine Learning: Leverage prediction & machine learning models
For storage, you have DocumentDB, Azure and blob storage, among other
HDInsight will help you in working with the data (big data) and make jobs with it.
Azure Web Apps and API apps will allow you to present and expose the data to you users and custom reports
Good luck
I have query related to learn the Cloud computing and Microsoft Azure. I am a .net programmer working on asp.net, C#, Sql server, WCF. If I learn the cloud computing then how it can help me in future as professional growth and How would it benefit me as a developer?
What should I learn specifically in cloud computing and Azure which would help me in future as professional growth.
To answer your first question, becoming familiar with cloud computing (even just Microsoft's platform) will help you to understand what is involved with deploying to the cloud, how cloud billing works, and understanding how elastic the environment is.
Specifically what to learn about Azure, I'd do the following:
Sign up for a 30-day azure pass.
Download the Azure Training
Kit.
Download the latest Azure SDK (1.4)
Work through some of the Training Kit examples in coordination with your 30-day free pass to Azure.
In addition to David's answer, I recommend Azure Fundamentals on the free portal Microsoft Learn. On Microsoft Learn there are many courses organized by role. You can just study or take also one or more certifications.
Other than that, notice that as a .NET developer you are not bound to the Microsoft Cloud solution, but you can learn and use any Cloud provider.