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The path key contains a path to the file relative to the config file, that defines your users and passwords. By default this is the users. In the hash key add the hashing algorithm to hash the passwords, for example, using md5 or any other algorithm supported by your operating system. The iterations key sets how many times the algorithm should be applied to the user's password, and keyLength is the length of the generated key. Implementing a new system in a regulated healthcare environment is complex.

Troubleshooting File-Based Authentication

The blueprint installs identification and resource permissions to help with these complexities. The blueprint also provides additional scripts and data used to simulate and study the results of admitting or discharging patients. These scripts allow staff to immediately begin to learn how to implement AI and ML using the solution in a safe, isolated scenario. The blueprint provides exceptional guidance and instructions for technical staff and also includes artifacts to help create a fully functional installation.

Publish a Rollback Blueprint File to Undo Changes

These other artifacts include:. A threat model for use with the Microsoft Threat Modeling Tool. This threat model shows components of the solution, the data flows between them, and the trust boundaries. The tool can be used for threat modeling by those looking to extend the base blueprint or for learning about the system architecture from a security perspective.

This shows what you the customer must provide versus what Microsoft provides for each requirement in the matrix. These resources are here on GitHub. There is little time investment to get up and running with this blueprint solution. A bit of PowerShell scripting knowledge is recommended, but step by step instructions are available to help guide the installation so technologists will be successful deploying this blueprint regardless of their scripting skills. Technical staff can expect to install the blueprint with little experience using Azure in 30 minutes to an hour.

The blueprint provides exceptional guidance and instructions for installation. It also provides scripting for install and uninstall of the blueprint services and resources. Calling the PowerShell deployment script is simple. Before the blueprint is installed, certain data must be collected and used as arguments to the deploy. Do not install the blueprint from a machine outside of Azure. The install is much more likely to succeed if you create a clean Windows 10 or other Windows VM in Azure and run the install scripts from there. This technique uses a cloud-based VM to mitigate latency and help to create a smooth installation.

During installation, the script calls out to other packages to load and use. When installing from a VM in Azure, the lag between the installation machine and the target resources will be much lower. However, some of the scripting packages downloaded are still vulnerable to latency as script packages live outside the Azure environment—which may lead to time-out failures. The installer downloads some external packages during installation. Sometimes, a script resource request will time out due to lag between the install machine and the package. When this happens, you have two choices:.

Define blueprint repositories

Run the install script again with no changes. The installer checks for already allocated resources and installs only those needed. While this technique can work, there is a risk the install script will try to allocate resources already in place. This can cause an error and the installation will fail. You still run the deploy. After the uninstall is done, change the prefix in the install script and try installing again. The latency issue may not occur again. If the installation fails while downloading script packages, run the uninstaller script and then the installer again.

This enables reconstituting the Key Vault if needed. If there is a need to reinstall the blueprint after an uninstall, you must change the prefix in the next deployment as the uninstalled Key Vault will cause an error if you do not change the prefix. The installing account must also be an Azure subscription administrator for the subscription being used. If the person doing the install is not in both of these roles, the install will fail.

A standard Azure account must be used. If needed, get a free trial with credit to spend for installing the blueprint solution and running its demos. However, more resources or services can be added to the Azure environment, making it a good test bed for additional initiatives, or a starting point for a production system. New resources, like Cosmos DB or a new Azure Functions , may be added to the solution as more Azure capabilities are needed. When adding new resources or services, ensure they are configured to meet security and privacy policies to remain compliant with regulations and policy.

This is a common prediction for healthcare providers to run as it helps in scheduling staffing and other operational decisions. Further, anomalies can be detected over time when an average length of stay for a given condition rises or declines. With the blueprint installed and all services working properly, the data to be analyzed can be ingested. Ingesting patient records is the first step in the using Azure Machine Learning Studio to run the patient length of stay experiment as shown below.

The example uses a trained model in an experiment to forecast patient length of stay based on many variables.

Publish a Rollback Blueprint File to Undo Changes

In this demonstration environment, the data ingested into the Azure SQL database is free from any defects or missing data elements. This data is clean. Missing data or incorrect values in the data will negatively impact results of the ML analysis. This often means valuable data is left unused or expensive consultants are brought in to create ML solutions. MLS is a web-based design environment used to create ML experiments. With MLS, you can create, train, evaluate, and score models, saving precious time when using different tools to develop models. MLS offers a complete toolset for ML workloads.

This means people new to ML can get a jump start using the tool and produce results faster than with other ML tools.

That lets your IT staff focus on providing value elsewhere and without bringing in a ML specialist. This capability in your own healthcare organization means various hypotheses can be tested and the resulting data analyzed for actionable insights, like patient interventionism offers pre-written modules to be used on a drag and drop canvas, visually composing end-to-end data-science workflows as experiments. There are pre-written modules that encapsulate specific algorithms such as decision trees, decision forests, clustering, time series, anomaly detection and others.

Custom modules can be added to any experiment. These are written in the R language or in Python. This allows using pre-built modules as well as custom logic to create a more sophisticated experiment.


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MLS enables creating and using learning models, as well as providing a set of pre-designed experiments for use in common applications. To save time, visit the Azure AI Gallery to find ready-to-use ML solutions for specific industries, including healthcare. For example, the gallery includes solutions and experiments for breast cancer detection and heart disease prediction.

Security and compliance are two of the most important things to be mindful of when creating, installing or managing software systems in a healthcare environment. The investment made in adopting a software system can be undercut by not meeting required security policies and certifications. Why not have multiple application objects? You can do that see Application Dispatching , but your applications will have separate configs and will be managed at the WSGI layer.

Blueprints instead provide separation at the Flask level, share application config, and can change an application object as necessary with being registered. The downside is that you cannot unregister a blueprint once an application was created without having to destroy the whole application object. The basic concept of blueprints is that they record operations to execute when registered on an application.

Flask associates view functions with blueprints when dispatching requests and generating URLs from one endpoint to another. This is what a very basic blueprint looks like. In this case we want to implement a blueprint that does simple rendering of static templates:. The first one is obviously from the application itself for the static files. As you can see, they are also prefixed with the name of the blueprint and separated by a dot.

On top of that you can register blueprints multiple times though not every blueprint might respond properly to that. In fact it depends on how the blueprint is implemented if it can be mounted more than once. Blueprints can provide resources as well. Sometimes you might want to introduce a blueprint only for the resources it provides.