This post is a collaboration between Divya Swarnkar and Kent Weare

We are excited to announce a new capability in Integration Environment that allows you to monitor Azure Integration Services, including Azure Logic Apps, Azure Service Bus, and Azure API Management (APIM), from a single pane of glass. This enhancement is designed to streamline your monitoring experience, providing a comprehensive view of your integration workflows and services. Here’s why this is important and how it will benefit you.

Integration Environment and Grouping AIS Resources

The Integration Environment was launched with a vision to provide a dedicated landing zone for Azure Integration Services. Alongside this, we introduced the capability to group AIS resources as an application. The unified monitoring experience leverages the Integration Environment as this landing zone, utilizing the grouping capability to organize AIS resources effectively as an application. This approach enhances the monitoring experience by aligning it with the application-centric view of your resources, making management and troubleshooting more intuitive and efficient.

Why Unified Monitoring Matters

Simplified Operations: With telemetry from Logic Apps, Service Bus, and APIM consolidated in a single Application Insights instance, you can view and manage your integration services more efficiently. This reduces the complexity of switching between multiple monitoring tools, allowing you to focus on what matters most – your business processes.

Enhanced Visibility: A unified monitoring dashboard offers a holistic view of your integration landscape. This comprehensive visibility helps in quickly identifying and diagnosing issues across different services, ensuring that your workflows run smoothly and without interruptions.

Improved Performance Monitoring: By centralizing telemetry, you can better analyze performance metrics and detect anomalies across all your integration services. This enables proactive troubleshooting and optimization, leading to improved reliability and performance of your applications.

Streamlined Troubleshooting: When issues arise, having all relevant data in one place accelerates the troubleshooting process. You can trace the root cause of problems across your Logic Apps, Service Bus, and APIM without having to piece together information from disparate sources.

How to Get Started

To start using this new capability, ensure that telemetry from all your resources in Azure Integration Services is directed to the same Application Insights instance. Select the appropriate instance from the dropdown menu in your monitoring dashboard, and you’re ready to go.

Here are the steps –

In Azure Portal, navigate to your Integration Environment resource and and application in it. For the selected application, go to Application monitoring > Insights.
Before you use the dashboard, select the Application Insights workspace from the drop-down menu.

There are three key tabs on this dashboard which are organized by the type of service in AIS. For each tab, there are subtabs which are organized based on the categories relevant for that service. Let’s look into each of them –

Logic Apps

The Overview tab provides an aggregated view into the Logic Apps and workflows in your application. You can look into the workflow level details in the worklow tab. For the workflows that needs more analysis, you can use the Runs tab to drill down into the run history and action failures. Finally, the Compute tab gives you the underlying compute utilization for these workloads.

Service Bus

For Service Bus, you have tabs to analyze the aggregated data for the entities that are in your application. The Requests tab helps you get more information on the Requests received and their status by the entities (queues/topics). The Messages tab likewise shows you the detailed information on the Messages in the entities and their status.

API Management

The Timeline provides time charts to see the overall trends for the requests and the response times. You can get detailed information about the status and processing times of each requests by APIs and Operations through the APIs and Operations tabs.

We believe this new feature will greatly enhance your monitoring experience and provide significant value to your integration projects. Stay tuned for more updates and enhancements as we continue to improve unified monitoring experience for Azure Integration Services.

Feedback opportunity

If you are interested in participating in 1:1 feedback session regarding operation and monitoring of integration applications, we would love to hear from you. Please fill this form (https://aka.ms/unifiedais-preview) and we’ll reach out to you.

To see the recorded demo of this capability, please watch this YouTube video.

Business Process Tracking Update

In addition to the unified monitoring investments made to Integration Environment, we also have some Business Process Tracking updates to share with you. The updates to Business Process Tracking are the direct result of customer feedback from organizations using our features launched at Ignite 2023.

Updates to Business Process Tracking include:

Business Process Tracking is a top level Azure Resource

Business Processes can be added to an Integration Application as part of our unified experience

Business Process stages now have two statues

Successful
Failed

Enhanced Token Picker

Transaction ID (formerly Business Id) now supports expressions
Variable support
Emit Boolean values at runtime

To try out the new Business Process Tracking capabilities, please perform the following steps:

In the Azure Portal, search for Business Process Tracking and click on the name of the service.

Click on Create to begin the provisioning process

Provide the required configuration details including the Transaction ID  and the Azure Data Explorer (ADX) details in the Storage tab.

Note: We are now referring to the Business ID as the Transaction ID to allow for more consistency in the offering. We have also moved the ADX configuration details from the Integration Application provisioning to the Business Process Tracking experience.

We now have an Overview page for your business process which acts as a landing page for the resource. To open up the designer, click on the Editor link.

Build out your Business Process by clicking on the + to add stages.
For each stage, provide data properties that you would like to track at that particular stage. You have the option to include both Success and Failure properties based upon your needs.

Note: Each stage must have a Success status modeled. Adding a Failure status is optional.

Save your work as you proceed in building your business process.
Once you have finished defining your stages, you can now begin the process of mapping your business process to the underlying Logic Apps implementation by toggling Show data source settings on.

For each stage you will need to select an Azure Subscription, Logic App and Workflow. Once you have configured these values, then click on the Select data sources link.

Where ever you have a Success or Failed status defined, you need to select a Point-in-time action which represents the trigger/action where you want to emit data to Azure Data Explorer. These point-in-time actions should be different between Success and Failure statuses.

As you complete your mappings, do Save and Validate your work

Once you have completed your mappings and have no validation errors, you can deploy your business process by clicking on the Deploy link.

After you have deployed your business process, tracking profiles will be injected into the Artifacts/Tracking Profiles directory in the Logic App(s) that are used in your mappings. Your Logic App will also restart and any new transactions should have their data tracked. You can view this data by clicking on the Transactions link.

You can drill into a specific transaction by clicking on the applicable Transaction ID (Ticket Number). When you do so, you will see an overlay of your business process and the underlying data

To view the data for a particular stage, click on it and you will see the data that was tracked during that stage.

With your solution now tracking your data, you can extend your experience by consuming this data in other analytics tools like Power BI or Azure Monitor Workbooks.

If you would like to see a recorded demo of this content, please watch the following YouTube video.

​Microsoft Tech Community – Latest Blogs –Read More 

I am trying to use the device preparation policies within our company

After signing in and waiting for it to reach 100% it says it can’t complete the setup because the apps can’t be installed but it will keep trying.

None of the apps have been installed

I go into the logs and i see these errors

The apps which I have selected to install are these

Some of these apps are all assigned to an “All Devices” group.

While others like Chrome, Teams and Dialpad are installed in a group called “Autopilot Devices without Hash” .

This group is the profile which I have used for the device preparation policies with Intune provisioning client as the owner.

The funny thing is the apps are then deployed once the user gets to the desktop screen which defeats the purpose of this exercise.

I’m not sure if I’m doing things right but I’ve followed two guides on how to get this working.

Any help would be appreciated.

​I am trying to use the device preparation policies within our company After signing in and waiting for it to reach 100% it says it can’t complete the setup because the apps can’t be installed but it will keep trying. None of the apps have been installed I go into the logs and i see these errors  The apps which I have selected to install are these  Some of these apps are all assigned to an “All Devices” group. While others like Chrome, Teams and Dialpad are installed in a group called “Autopilot Devices without Hash” .This group is the profile which I have used for the device preparation policies with Intune provisioning client as the owner. The funny thing is the apps are then deployed once the user gets to the desktop screen which defeats the purpose of this exercise. I’m not sure if I’m doing things right but I’ve followed two guides on how to get this working.Any help would be appreciated.  Read More

​

The generative AI landscape is in a constant state of flux, with new developments emerging at a breakneck pace. In recent times along with LLMs we have also seen the rise of SLMs. From virtual assistants to chatbots, SLMs are revolutionizing how we interact with technology through conversation. As the backbone of many conversational models, SLMs enable natural language understanding and generation, leading to more engaging user experiences.

The deployment of large language models (LLMs) and smaller language models (SLMs) on local infrastructure has emerged as a critical area of discussion due to several compelling factors. These factors include maintaining stringent data privacy regulations, achieving cost-effectiveness over time, and enabling greater flexibility for customization and integration.

AI Toolkit (Earlier known as Windows AI Studio) is here to address such problems, some major problems this solves is,

Onboarding the LLMs/ SLMs on our local machines. This toolkit lets us to easily download the models on our local machine.
 Evaluation of the model. Whenever we need to evaluate a model to check for the feasibility to any particular application, then this tool lets us do it in a playground environment, which is what we will seeing in this blog.
Fine-tuning, this majorly delas with training the model further to do the tasks that we specifically want the model to do. Usually, it does a generic task and has generic data, with fine-tuning we can give it a particular flavor to perform particular task.

The best part is that it runs on windows machine and has models which are optimized for windows machine. The AI toolkit lets the models run locally and makes it offline capable. AI toolkit opens up plethora of scenarios for organizations in various sectors like healthcare, education, banking, governments and so on.

Bring AI development into your VS Code workflow with the AI Toolkit extension. It empowers you to:

Run pre-optimized AI models locally: Get started quickly with models designed for various setups, including Windows 11 running with DirectML acceleration or direct CPU, Linux with NVIDIA GPUs, or CPU-only environments.

Test and integrate models seamlessly: Experiment with models in a user-friendly playground or use a REST API to incorporate them directly into your application.

Fine-tune models for specific needs: Customize pre-trained models (like popular SLMs Phi-3 and Mistral) locally or in the cloud to enhance performance, tailor responses, and control their style.

Deploy your AI-powered features: Choose between cloud deployment or embedding them within your device applications.

Alright! Now let’s experience this amazing extension on our machines using Visual Studio Code.  Since this is available as VS Code extension, Visual Studio code is a direct prerequisite to use this tool. Use this link to download VSCode on your machines.

We can run AI Toolkit Preview directly on local machine.  However, certain tasks might only be available on Windows or Linux depending on the chosen model. Mac support is on the way!

For local run on Windows + WSL, WSL Ubuntu distro 18.4 or greater should be installed and is set to default prior to using AI Toolkit. Learn more how to install Windows subsystem for Linux and changing default distribution or I have explained it step-wise in one of the previous blog where I have demonstrated the installation of windows AI studio. You can find it here. Steps of installation of WSL remains the same as explained in that blog. Windows AI Studio is deprecated and is now rebranded as AI Toolkit. For the latest documentation and to download and use the AI Toolkit, please visit the GitHub page.

Once the WSL is installed, launch the Ubuntu terminal and type the following,

code .

This should launch the Visual studio code. Since this is the first launch, it will collect few things.

Now Visual studio code window will be launched.

Note that this will be in the remote connection with session name as WSL Ubuntu. Now the extensions we will be installing will be done in WSL.

On the activity bar Visual Studio Code window, there is an “Extension” option 

.

Click on this and search for “AI Toolkit” and install the extension, once it is installed, we can see an extra icon on the activity bar.

Once it is installed, a new extension will be visible on the left side menu, when clicked on it, a pop-up notification comes up showcasing the port forwarding capabilities and also auto assigns one port for the Toolkit.

Also, two fresh sections are shown under AI-toolkit namely Models and Resources.

Models section contains the following,

Model Catalog

Resources section contains the following,

Model Playground
Model Finetuning

Models contains the Model Catalog which is basically the list of all the available AI Models. This is where we can choose and download a model which fits our use case.AI Toolkit offers the collection of publicly available AI models already optimized for Windows. The models are stored in the different locations including Hugging Face, GitHub and others, but we can browse the models and find all of them in one place ready for downloading and using in windows application.

We can also find the model cards for each of the model, to check various parameters of the model in order to further decide which one to choose for a particular application. Few more details like, number of parameters the model is pre-trained on, Dependency on CPU or GPU, the size of the model is all available here. Finally, upon deciding, the model can be downloaded using the “Download” button for each model. Any number of models can be downloaded.

For the purpose of this demonstration, I will download Mistral-mistral-7b-v02-int4-gpu and one of the recent SLM of Microsoft Phi-3-mini-128k-cuda-int4-onnx

Note: For optimized performance on Windows devices that have at least one GPU, select model versions that only target Windows. This ensures you have a model optimized for the DirectML accelerator. The model names are in the format of {model_name}-{accelerator}-{quantization}-{format}.

To check whether you have a GPU on your Windows device, open Task Manager and then select the Performance tab. If you have GPU(s), they will be listed under names like “GPU 0” or “GPU 1”.

The next interesting part is the Model playground which is available in the resources section. For the models that we have evaluated using model card and downloaded, its time now to test them out using the Playground!

Playground has multiple sections, let’s see each one.

Model: This is the placeholder which lets us load the model. In this case I will be using the Phi-3-mini-128k-cuda-int4-onnx.
Context Instructions: This is the system prompt for the model. It guides the model the way in which it has to behave to a particular scenario. For example, we can ask it to respond in a Shakespearean tone, and it will respond accordingly. I will input “Respond in Shakespearean accent” as the Context Instruction.
Inference Parameters: These are the adjustment parameters for the model. Under this section we have Max response length (tokens), Temperature, Top P, Frequency Penalty, Presence penalty. Hovering over the small “i” icon explains about each parameter.
Chat Area: This is where we type in our messages and finally engage in a chat conversation with the model. The model responds on the pretrained data.

Note: Some machines might show the error as follows while loading the model in the playground.

Failed loading model Phi-3-mini-128k-cuda-int4-onnx: /onnxruntime_src/onnxruntime/core/session/provider_bridge_ort.cc:1426 onnxruntime::Provider& onnxruntime::ProviderLibrary::Get() [ONNXRuntimeError] : 1 : FAIL : Failed to load library libonnxruntime_providers_cuda.so with error: libcudnn.so.8: cannot open shared object file: No such file or directory

This is majorly due to some missing libraries, to fix it, execute the following commands one by one at a time in the Ubuntu terminal or the VS Code terminal of the WSL session.

• pip install onnxruntime
• pip install onnxruntime-gpu
• cd /usr/local/cuda/lib64
• ls
• sudo apt install nvidia-cudnn
• sudo apt update
• apt list –upgradable
• sudo apt upgrade
• sudo apt update
• sudo apt update —fix-missing
• sudo apt-get install libcudnn8
• sudo apt update

This must help you resolve the error!

It is now responding in Shakespearean accent because of the Context information.

We can further evaluate the model based on our needs and the best part is that it is absolutely free and is now running on Local machine!! AI toolkit thus solves a major problem and also helps us in streamlining the development of GenAI applications. In the further blogs let’s see how to interact with the model using Python and build some cool applications. Stay tuned!

​Microsoft Tech Community – Latest Blogs –Read More