Azure ML Experimentation
Hi - I have created a Machine Learning Expirementation account on Azure, which I now wish to cancel. When I do so, it tells me that I cannot delete the resource until all nested resources are deleted. I have already deleted the storage account, all that is left is the workspace, which does not have an option to delete. Any Suggestions?Azure Bot Service - Build your own bots
This video shows the practical use of bots using the Azure Bot Service to infuse artificial intelligence within your customer relationship management. You'll see how bots work and how to build your own. Details: Intelligent interactions with natural language and the ability to detect customer sentiment and intent, triaging and transacting the customer engagement process to make opportunistic and personalized recommendations, and even how you can quickly get bots up and running for yourself with the Azure Bot Service.
Visual Intelligence Made Easy
Easily customize your own state-of-the-art computer vision models that fit perfectly with your unique use case. Just bring a few examples of labeled images and let Custom Vision do the hard work. New Cognitive Service Release - Visit for more information : Custom Vision ServicePublishing Azure Machine Learning Webservices the self-service way
Publishing Azure Machine Learning Webservices the self-service way Giving your data-analysts what they need with least privileges in Azure. Problem description Azure Machine Learning offers the possibility to publish predictive models as an Azure Machine Learning Webservice. These webservices can be used to make predictions based on new data. To publish Azure Machine Learning Webservices, according to the Microsoft documentation Contributor rights are needed. This contradicts with our company’s security policy: the Contributor role is able to create anything in your subscription and might cause a security breach. Since we are the infrastructure as code provider within our company we do not want that to happen. Background In our organization we have 3 departments that are involved in publishing web services: Cloud Integration Creates a self-service capability to provision Azure Machine Learning workspaces via a CI/CD pipeline (VSTS) Customer Data & Analytics Manage the workspaces: assign workspaces to the Data Analysts Create a Machine Learning Commitment Plan that is used to host the Azure Machine Learning Webservices Data Analysts Create and refine the Machine Learning Models in their Machine Learning Workspace Publish the models as Azure Machine Learning Webservices to an existing Commitment Plan, using the Workspace option “Publish Webservice” which redirects Solution Authorizations in Azure are granted based upon Roles. These roles can be connect to AAD groups and users. There are no default Azure Roles available that can be used to assign the needed authorizations to only grant access to the resources that are needed to manage webservices and commitment plans. We have created new Custom Roles for the Data Tech Team and the Data Analysts. This are the authorizations that we found out were needed for the Data Tech team: Microsoft.Resources/subscriptions/resourceGroups/read Microsoft.MachineLearning/commitmentPlans/* Microsoft.MachineLearning/Workspaces/read Microsoft.MachineLearning/webServices/read Microsoft.MachineLearning/Workspaces/listworkspacekeys/action For the Data Analysts that need to publish the webservices we defined a Custom Role with the following access: Microsoft.Storage/storageAccounts/listkeys/action Microsoft.Resources/subscriptions/resourceGroups/read Microsoft.Storage/storageAccounts/read Microsoft.MachineLearning/webServices/* Microsoft.MachineLearning/commitmentPlans/read Microsoft.MachineLearning/commitmentPlans/commitmentAssociations/read Microsoft.MachineLearning/Workspaces/read Microsoft.MachineLearning/Workspaces/write Microsoft.MachineLearning/Workspaces/listworkspacekeys/action We assign the custom roles on resourcegroup level, and link the user (via Azure Active Directory groups) to the roles. This way we are sure that the right people can deploy the Commitmentplans and the Azure Machine Learning Webservices only, so we stay in control of what is deployed to our Azure subscriptions. One caveat: the CommitmentPlan can be deployed to a separate resourcegroup, but you cannot choose the resourcegroup where the WebServices are deployed. They are deployed to the resourcegroup where the Machine Learning Workspace resides. How did we do it We had to find out with trial and error what authorizations were needed. We used one of the sample experiments to publish a webservice via Machine Learning Studio workspace, and checked the event logging in ARM to see in what stage the deployment failed. We saw no error in the ARM logging when we deployed a web services using a user with insufficient authorizations at first. But after granting more permissions on the resource group level we could at least see what authorizations were needed to deploy the webservices. The Microsoft.Storage/storageAccounts/listkeys/action authorization was essential: after we had granted our test user this access we could further pinpoint which other resources were accessed.
Machine Learning
Hi, I am a Data Developer and I am very interested in understanding and learning Machine Learning (especially Azure ML). I read the pdf suggested "INTRODUCING AZURE MACHINE LEARNING - A GUIDE FOR TECHNICAL PROFESSIONALS" by David Chappell and I would like to know what to read next and how can I make experiments so I can understand better and become a Data Scientist. Or what trainings or tutorials should I follow? I am asking because I'm very interested in this field of technology and I would like to work in it. I have a very logical thinking and a very sharp mind and I really believe that this would be the perfect job for me. I appreciate your help. King regards, Andreea (a data dev who wants to become an awesome data scientist working with ML)Visualize Azure Machine Learning Models with MicroStrategy Desktop™
Have you ever wondered how you can use machine learning in your work? It would be easy to assume that this type of advanced technology isn’t available to you because of simple logistics or complexities. The truth is, machine learning is more accessible than ever before – and even easy-to-use. Together, Microsoft and MicroStrategy can help users create powerful, cloud-based machine learning applications through self-service analytics. MicroStrategy Desktop™, combined with Microsoft Azure ML, uses a drag-n-drop interface so users can efficiently plan, create and glean insights from a predictive dashboard. Learn more about it on the Azure blog.How to get started with Azure Machine Learning?
This was a question from a university researcher in the Pacific northwest. The good news is that Azure Machine Learning or Azure ML is a powerful native Platform as a Service (PaaS) offering which has been around since 2015. Mark Garcia, a Cloud Solution Architect at Microsoft, put together a FAQ around Azure Machine Learning: What is our definition of Machine Learning? When you think ML many different things fall into this like AI, neural networks, predictive outcomes. Our ML definition is simple: “Experience” = past data + human input. Past data is often huge – the quantity of data is doubling about every 18 months and that’s only increasing from here. Computers can consider far more variables than a human making the same decision. And what do we mean by human input? Human input takes two forms – the input of the user who is either communicating that the output is what they are looking to see or not. In the case that it’s not, the machine can either self-adjust to deliver better results moving forward or the advanced analytic developer or data scientist can make those changes to the model. How does Azure ML work? Here is a flow diagram for Azure ML: Read more on: Where to go to understand what Azure ML provides How to get started with Azure ML Useful hands on labs you can use to see what Azure ML can do Languages supported by Azure ML Azure ML APIs and Azure ML Experiments published for you to leverage Data sources are supported for Azure ML You can fin these and other topics at Mark`s blog post.Azure IoT Pipelines with Microsoft AzureDevOps Project for CI/CD
Azure IoT Edge – Hub with Azure DevOps Pipeline Configure continuous integration (CI) and continuous delivery (CD) for your IoT Edge application with DevOps Projects. DevOps Projects simplifies the initial configuration of a build and release pipeline in Azure Pipelines. In the following steps you can see how easy it is to build your Continuous integration and continuous deployment to Azure IoT Edge with DevOps Project. Conclusion : When you connect Microsoft Azure IoT Edge – HUB with your Internet of Things Devices and combine it with Microsoft Azure DevOps Team to develop your Azure IoT Pipeline, then you are in fully control of Continuous integration and continuous deployment to Azure IoT Edge. From here you can make your innovations and Intelligent Cloud & Edge with Artificial Intelligence and Machine Learning to your Devices. You will see that this combination will be Awesome for HealthCare, Smart Cities, Smart Buildings, Infrastructure, and the Tech Industry.
