I’m trying to get data from a multi line text field with append changes on it to appear on a custom search page built using the PnP Search web parts. I haven’t had any luck mapping it successfully to a managed property after trying for about 2 weeks. I’ve tried using the RefinableString properties, and two text managed properties that have Searchable, Queryable, Retrievable and one with Allow multiple values enabled (Allow multiple values: Allow multiple values of the same type in this managed property. For example, if this is the “author” managed property, and a document has multiple authors, each author name will be stored as a separate value in this managed property.) All return null. There is content in the fields, the list has been reindexed a few times and it’s been over 48 hours. 

I’m beginning to think it isn’t possible seeing as you have to jump through a few hoops to get the values with REST calls. Does anyone know if it is possible?

​I’m trying to get data from a multi line text field with append changes on it to appear on a custom search page built using the PnP Search web parts. I haven’t had any luck mapping it successfully to a managed property after trying for about 2 weeks. I’ve tried using the RefinableString properties, and two text managed properties that have Searchable, Queryable, Retrievable and one with Allow multiple values enabled (Allow multiple values: Allow multiple values of the same type in this managed property. For example, if this is the “author” managed property, and a document has multiple authors, each author name will be stored as a separate value in this managed property.) All return null. There is content in the fields, the list has been reindexed a few times and it’s been over 48 hours.  I’m beginning to think it isn’t possible seeing as you have to jump through a few hoops to get the values with REST calls. Does anyone know if it is possible?  Read More

​

Hi there, 

we are trying to enroll our US business location for CSP indirect reseller (for our DE location we are successfully registered and enrolled). 

I created an Entra tenant and used the enrollment form, but I fail when completing the form to kick everything off. I receive the below error message: 

We have one central website, but it won’t accept the entry. What can i provide to make this work? 

I can not even open a support request, because I end up in a closed form when i follow the red link : 

Any recommendations and ideas are really welcome. 

Thanks 

Ann

​Hi there, we are trying to enroll our US business location for CSP indirect reseller (for our DE location we are successfully registered and enrolled). I created an Entra tenant and used the enrollment form, but I fail when completing the form to kick everything off. I receive the below error message:  We have one central website, but it won’t accept the entry. What can i provide to make this work? I can not even open a support request, because I end up in a closed form when i follow the red link :  Any recommendations and ideas are really welcome. Thanks Ann  Read More

​

I have to format a Surface 10 Pro Business without using a recovery image but using the Windows 11 key. The SSD disk is not recognized because the Sata controller driver is missing. Can anyone tell me the model or the driver download link? Thank you

​I have to format a Surface 10 Pro Business without using a recovery image but using the Windows 11 key. The SSD disk is not recognized because the Sata controller driver is missing. Can anyone tell me the model or the driver download link? Thank you  Read More

​

Bonjour,

je suis en windows 10 22h2 build 19045.2787

quelles sont les manips pour l’upgrader, car il est figé .

Merci

​Bonjour,je suis en windows 10 22h2 build 19045.2787quelles sont les manips pour l’upgrader, car il est figé .Merci    Read More

​

The Future of AI: Distillation Just Got Easier

Part 3 – Deploying your LoRA Fine-tuned Llama 3.1 8B model, why it’s a breeze!

Learn how Azure AI makes it effortless to deploy your LoRA fine-tuned models using Azure AI.  (🚀🔥 Github recipe repo).

By Cedric Vidal, Principal AI Advocate, Microsoft

Part of the Future of AI 🚀 series initiated by Marco Casalaina with his Exploring Multi-Agent AI Systems blog post.

A Llama on a rocket launched in space, generated using Azure OpenAI DALL-E 3

Welcome back to our series on leveraging Azure AI Studio to accelerate your AI development journey. In our previous posts, we’ve explored synthetic dataset generation and the process of fine-tuning models. Today, we’re diving into the crucial step that turns your hard work into actionable insights: deploying your fine-tuned model. In this installment, we’ll guide you through deploying your model using Azure AI Studio and the Python SDK, ensuring a seamless transition from development to production.

Why Deploying GPU Accelerated Inference Workloads is Hard

Deploying GPU-accelerated inference workloads comes with a unique set of challenges that make the process significantly more complex compared to standard CPU workloads. Below are some of the primary difficulties encountered:

GPU Resource Allocation: GPUs are specialized and limited resources, requiring precise allocation to avoid wastage and ensure efficiency. Unlike CPUs that can be easily provisioned in larger numbers, the specialized nature of GPUs means that effective allocation strategies are crucial to optimize performance.
GPU Scaling: Scaling GPU workloads is inherently more challenging due to the high cost and limited availability of GPU resources. it requires careful planning to balance cost efficiency with workload demands, unlike more straightforward CPU resource scaling.
Load Balancing for GPU Instances: Implementing load balancing for GPU-based tasks is complex due to the necessity of evenly distributing tasks across available GPU instances. This step is vital to prevent bottlenecks, avoid overload in certain instances, and ensure optimal performance of each GPU unit.
Model Partitioning and Sharding: Large models that cannot fit into a single GPU memory require partitioning and sharding. This process involves splitting the model across multiple GPUs, which introduces additional layers of complexity in terms of load distribution and resource management.
Containerization and Orchestration: While containerization simplifies the deployment process by packaging models and dependencies, managing GPU resources within containers and orchestrating them across nodes adds another layer of complexity. Effective orchestration setups need to be fine-tuned to handle the subtle dynamics of GPU resource utilization and management.
LoRA Adapter Integration: LoRA, which stands for Low-order Rank Adaptation, is a powerful optimization technique that reduces the number of trainable parameters by decomposing the original weight matrices into lower-rank matrices. This makes it efficient for fine-tuning large models with fewer resources. However, integrating LoRA adapters into deployment pipelines involves additional steps to efficiently store, load and merge the lightweight adapters with the base model and serve the final model, which increases the complexity of the deployment process.
Monitoring GPU Inference Endpoints: Monitoring GPU inference endpoints is complex due to the need for specialized metrics to capture GPU utilization, memory bandwidth, and thermal limits, not to mention model specific metrics such as token counts or request counts. These metrics are vital for understanding performance bottlenecks and ensuring efficient operation but require intricate tools and expertise to collect and analyze accurately.
Model Specific Considerations: It’s important to acknowledge that the deployment process is often specific to the base model architecture you are working with. Each new version of a model or a different model vendor will require a fair amount of adaptations in your deployment pipeline. This could include changes in preprocessing steps, modifications in environment configurations, or adjustments in the integration or versions of third-party libraries. Therefore, it’s crucial to stay updated with the model documentation and vendor-specific deployment guidelines to ensure a smooth and efficient deployment process.
Model Versioning Complexity: Keeping track of multiple versions of a model can be intricate. Each version may exhibit distinct behaviors and performance metrics, necessitating thorough evaluation to manage updates, rollbacks, and compatibility with other systems. We’ll cover the subject of model evaluation more thoroughly in the next blog post. Another difficulty with versioning is storing the weights of the different LoRA adapters and keeping track of the versions of the base models they must be adapted onto.
Cost Planning: Planning the costs for GPU inference workloads is challenging due to the variable nature of GPU usage and the higher costs associated with GPU resources. Predicting the precise amount of GPU time required for inference under different workloads can be difficult, leading to unexpected expenses.

Understanding and addressing these difficulties is crucial for successfully deploying GPU-accelerated inference workloads, ensuring that the full potential of GPU capabilities is harnessed.

Azure AI Serverless: A Game Changer

Azure AI Serverless is a game changer because it effectively addresses a lot of challenges with deploying GPU-accelerated inference workloads. By leveraging the serverless architecture, it abstracts away the complexities associated with GPU resource allocation, model specific deployment considerations, and API management. This means you can deploy your models without worrying about the underlying infrastructure management, allowing you to focus on your application’s needs. Additionally, Azure AI Serverless supports a diverse collection of models and abstracts away the choice and provisioning of GPU hardware accelerators, ensuring efficient and fast inference times. The platform’s integration with managed services enables robust container orchestration, simplifying the deployment process even further and enhancing overall operational efficiency.

Attractive pay as you go cost model

One of the standout features of Azure AI Serverless is its token-based cost model, which greatly simplifies cost planning. With token-based billing, you are charged based on the number of tokens processed by your model, making it easy to predict costs based on expected usage patterns. This model is particularly beneficial for applications with variable loads, as you only pay for what you use.

Because the managed infrastructure needs to maintain LoRA adapters in memory and swap them on demand, there is an additional per hour cost associated with fine tuned serverless endpoints but it is billed by the hour only while the endpoint is being used. This makes it super easy to plan ahead future bills depending on your expected usage profile.

Also, the hourly cost is meant to go down, it already went down dramatically from $3.09/hour for a Llama 2 7B based model to $0.74/hour for a Llama 3.1 8B based model.

By paying attention to these critical factors, you can ensure that your model deployment is robust, secure, and capable of meeting the demands of your application.

Region Availability

When deploying your Llama 3.1 fine-tuned model, it’s important to consider the geographical regions where the model can be deployed. As of now, Azure AI Studio supports the deployment of Llama 3.1 fine-tuned models in the following regions: East US, East US 2, North Central US, South Central US, West US, and West US 3. Choosing a region that’s closer to your end-users can help reduce latency and improve performance. Ensure you select the appropriate region based on your target audience for optimal results.

For the most up-to-date information on region availability for other models, please refer to this guide on deploying models serverlessly.

Let’s get coding with Azure AI Studio and the Python SDK

Before proceeding to deployment, you’ll need a model that you have previously fine-tuned. One way is to use the process described in the two preceding installments of this fine-tuning blog post series: the first one covers synthetic dataset generation using RAFT and the second one covers fine-tuning. This ensures that you can fully benefit from the deployment steps using Azure AI Studio.

Note: All code samples that follow have been extracted from the 3_deploy.ipynb notebook of the raft-recipe GitHub repository. The snippets have been simplified and some intermediary steps left aside for ease of reading. You can either head over there, clone the repo and start experimenting right away or stick with me here for an overview.

Step 1: Set Up Your Environment

First, ensure you have the necessary libraries installed. You’ll need the Azure Machine Learning SDK for Python. You can install it using pip:

pip install azure-ai-ml

Next, you’ll need to import the required modules and authenticate your Azure ML workspace. This is standard, the MLClient is the gateway to the ML Workspace which gives you access to everything AI and ML on Azure.

from azure.ai.ml import MLClient
from azure.identity import (
DefaultAzureCredential,
InteractiveBrowserCredential,
)
from azure.ai.ml.entities import MarketplaceSubscription, ServerlessEndpoint

try:
credential = DefaultAzureCredential()
credential.get_token(“https://management.azure.com/.default”)
except Exception as ex:
credential = InteractiveBrowserCredential()

try:
client = MLClient.from_config(credential=credential)
except:
print(“Please create a workspace configuration file in the current directory.”)

# Get AzureML workspace object.
workspace = client._workspaces.get(client.workspace_name)
workspace_id = workspace._workspace_id

Step 2: Resolving the previously registered fine-tuned model

Before deploying, you need to resolve your fine-tuned model in the Azure ML workspace.

Since the fine-tuning job might still be running, you may want to wait for the model to be registered, here’s a simple helper function you can use.

def wait_for_model(client, model_name):
“””Wait for the model to be available, typically waiting for a finetuning job to complete.”””
import time

attempts = 0
while True:
try:
model = client.models.get(model_name, label=”latest”)
return model
except:
print(f”Model not found yet #{attempts}”)
attempts += 1
time.sleep(30)

The above function is basic but will make sure your deployment can proceed as soon as your model becomes available.

print(f”Waiting for fine tuned model {FINETUNED_MODEL_NAME} to complete training…”)
model = wait_for_model(client, FINETUNED_MODEL_NAME)
print(f”Model {FINETUNED_MODEL_NAME} is ready”)

Step 3: Subscribe to the model provider

Before deploying a model fine-tuned using a base model from a third-party non-Microsoft source, you need to subscribe to the model provider’s marketplace offering. This subscription allows you to access and use the model within Azure ML.

print(f”Deploying model asset id {model_asset_id}”)

from azure.core.exceptions import ResourceExistsError
marketplace_subscription = MarketplaceSubscription(
model_id=base_model_id,
name=subscription_name,
)

try:
marketplace_subscription = client.marketplace_subscriptions.begin_create_or_update(marketplace_subscription).result()
except ResourceExistsError as ex:
print(f”Marketplace subscription {subscription_name} already exists for model {base_model_id}”)

Details on how to construct the base_model_id and subscription_name are available in the 3_deploy.ipynb notebook.

Step 4: Deploy the model as a serverless endpoint

This section manages the deployment of a serverless endpoint for your fine-tuned model using the Azure ML client. It checks for an existing endpoint and creates one if it doesn’t exist, then proceeds with the deployment.

from azure.core.exceptions import ResourceNotFoundError
try:
serverless_endpoint = client.serverless_endpoints.get(endpoint_name)
print(f”Found existing endpoint {endpoint_name}”)
except ResourceNotFoundError as ex:
serverless_endpoint = ServerlessEndpoint(name=endpoint_name, model_id=model_asset_id)
serverless_endpoint = client.serverless_endpoints.begin_create_or_update(serverless_endpoint).result()

print(“Waiting for deployment to complete…”)
serverless_endpoint = ServerlessEndpoint(name=endpoint_name, model_id=model_id)

created_endpoint = client.serverless_endpoints.begin_create_or_update(serverless_endpoint).result()
print(“Deployment complete”)

Step 5: Check that the endpoint is correctly deployed

As part of a deployment pipeline, it is a good practice to include integration tests that check that the model is correctly deployed and fails fast instead of waiting for steps down the line to fail without context.

import requests

url = f”{endpoint.scoring_uri}/v1/chat/completions”

prompt = “What do you know?”
payload = {
“messages”:[ { “role”:”user”,”content”: prompt } ],
“max_tokens”:1024
}
headers = {“Content-Type”: “application/json”, “Authorization”: endpoint_keys.primary_key}

response = requests.post(url, json=payload, headers=headers)

response.json()

This code assumes that the deployed model is a chat model for simplicity. The code available in the 3_deploy.ipynb notebook is more generic and will cover both completion and chat models.

Conclusion

Deploying your fine-tuned model with Azure AI Studio and the Python SDK not only simplifies the process but also empowers you with unparalleled control, ensuring you have a robust and reliable platform for your deployment needs.

Stay tuned for our next blog post, in two weeks we will delve into assessing the performance of your deployed model through rigorous evaluation methodologies. Until then, head out to the Github repo and happy coding!

​Microsoft Tech Community – Latest Blogs –Read More 

Salam

I have a question regarding official training for Exchange Server 2019. I’m aware that Microsoft offers various training materials for its products, but I couldn’t find any official course specifically designed for Exchange 2019 in the catalog.

Could someone confirm if there was ever an official training course or certification for Exchange Server 2019? I’ve seen training for previous versions like Exchange 2010, but it seems like there wasn’t anything equivalent for 2019. Any clarification would be appreciated.

​SalamI have a question regarding official training for Exchange Server 2019. I’m aware that Microsoft offers various training materials for its products, but I couldn’t find any official course specifically designed for Exchange 2019 in the catalog.Could someone confirm if there was ever an official training course or certification for Exchange Server 2019? I’ve seen training for previous versions like Exchange 2010, but it seems like there wasn’t anything equivalent for 2019. Any clarification would be appreciated.  Read More

​

by Ganesh Ananthanarayanan, Xenofon Foukas, Bozidar Radunovic, Yongguang Zhang

Introduction to the Evolution of RAN

The development of Cellular Radio Access Networks (RAN) has reached a critical point with the transition to 5G and beyond. This shift is motivated by the need for telecommunications operators to lower their high capital and operating costs while also finding new ways to generate revenue. The introduction of 5G has transformed traditional, monolithic base stations by breaking them down into separate, virtualized components that can be deployed on standard, off-the-shelf hardware in various locations. This approach makes it easier to manage the network’s lifecycle and accelerates the release of new features. Additionally, 5G has promoted the use of open and programmable interfaces and introduced advanced technologies that expand network capacity and support a wide range of applications.

As we enter the era of 5G Advanced and 6G networks, the goal is to maximize the network’s potential by solving the complex issues brought by the added complexity of 5G and introducing new applications that offer unique value. In this emerging landscape, AI stands out as a critical component, with advances in generative AI drawing significant interest from the telecommunications sector. AI’s proficiency in pattern recognition, traffic prediction, and solving intractable problems like scheduling makes it an ideal solution for these and many other longstanding RAN challenges. There is a growing consensus that future mobile networks should be AI-native, with both industry and academia offering support for this trend. However, practical hurdles like data collection from distributed sources and handling the diverse characteristics of AI RAN applications remain obstacles to be overcome.

The Indispensable Role of AI in RAN

The need for AI in RAN is underscored by AI’s ability to optimize and enhance critical RAN functions like network performance, spectrum utilization, and compute resource management. AI serves as an alternative to traditional optimization methods, which struggle to cope with the explosion of search space due to complex scheduling, power control, and antenna assignments. With the infrastructure optimization problems introduced by 5G (e.g. server failures, software bugs), AI shows promise through predictive maintenance and energy efficiency management, presenting solutions to these challenges that were previously unattainable. Moreover, AI can leverage the open interfaces exposed by RAN functions, enabling third-party applications to tap into valuable RAN data, enhancing capabilities for additional use cases like user localization and security.

Distributed Edge Infrastructure and AI Deployment

As AI becomes increasingly integrated into RAN, choosing the optimal deployment location is crucial for performance. The deployment of AI applications in RAN depends on where the RAN infrastructure is located, ranging from the far edge to the cloud. Each location offers different computing power and has its own trade-offs in resource availability, bandwidth, latency, and privacy. These factors are important when deciding the best place to deploy AI applications, as they directly affect performance and responsiveness. For example, while the cloud provides more computing resources, it may also cause higher latency, which can be problematic for applications that need real-time data processing or quick decision-making. 

Addressing the Challenges of Deploying AI in RAN

Deploying AI in RAN involves overcoming various challenges, particularly in the areas of data collection and application orchestration. The heterogeneity of AI applications’ input features makes data collection a complex task. Exposing raw data from all potential sources isn’t practical, as it would result in an overwhelming volume of data to be processed and transmitted. The current industry approach of utilizing standardized APIs for data collection is not always conducive to the development of AI-native applications. The standard set of coarse-grained data sources exposed through these APIs often fail to meet the nuanced requirements of AI-driven RAN solutions. This limitation forces developers to adapt their AI applications to the available data rather than collecting the data that would best serve the application’s needs.

The challenge of orchestrating AI RAN applications is equally daunting. The dispersed nature of the RAN infrastructure raises questions about where the various components of an AI application should reside. These questions require a careful assessment of the application’s compute requirements, response latency, privacy constraints, and the varied compute capabilities of the infrastructure. The complexity is further amplified by the need to accommodate multiple AI applications, each vying for the same infrastructure resources. Developers are often required to manually distribute these applications across the RAN, a process that is not scalable and hinders widespread deployment in production environments.

A Vision for a Distributed AI-Native RAN Platform

To address these challenges, we propose a vision for a distributed AI-native RAN platform that is designed to streamline the deployment of AI applications. This platform is built on the principles of flexibility and scalability, with a high-level architecture that includes dynamic data collection probes, AI processor runtimes, and an orchestrator that coordinates the platform’s operations. The proposed platform introduces programmable probes that can be injected at various points in the platform and RAN network functions to collect data tailored to the AI application’s requirements. This approach minimizes data volume and avoids delays associated with standardization processes.

The AI processor runtime is a pivotal component that allows for the flexible and seamless deployment of AI applications across the infrastructure. It abstracts the underlying compute resources and provides an environment for data ingestion, data exchange, execution, and lifecycle management. The runtime is designed to be deployed at any location, from the far edge to the cloud, and to handle both AI RAN and non-RAN AI applications.

The orchestrator is the component that brings all this together, managing the placement and migration of AI applications across various runtimes. It also considers the developer’s requirements and the infrastructure’s capabilities to optimize the overall utility of the platform. The orchestrator is dynamic, capable of adapting to changes in resource availability and application demands, and can incorporate various policies that balance compute and network load across the infrastructure.

In articulating the vision for a Distributed AI-Native RAN platform, it is important to clarify that the proposed framework does not impose a specific architectural implementation. Instead, it defines high-level APIs and constructs that form the backbone of the platform’s functionality. These include a data ingestion API that facilitates the capture and input of data from various sources, a data exchange API that allows for the communication and transfer of data between different components of the platform, and a lifecycle management API that oversees the deployment, updating, and decommissioning of AI applications. The execution environment within the platform is designed to be flexible, promoting innovation and compatibility with major hardware architectures such as CPUs and GPUs. This flexibility ensures that the platform can support a wide range of AI applications and adapt to the evolving landscape of hardware technologies.

Moreover, to demonstrate the feasibility and potential of the proposed platform, we have internally prototyped a specialized and efficient implementation of the AI processor, particularly for the far edge. This prototype is carefully designed to work with fewer CPUs, optimizing resource use while maintaining high performance. It demonstrates that the AI processor runtime principles can be implemented effectively to meet the specific needs of the far edge, where resources are limited and real-time processing is crucial. This specialized implementation exemplifies the targeted innovation that the platform emphasizes, showcasing how the flexible execution environment can be tailored to address specific challenges within the RAN ecosystem.

Balancing Open and Closed Architectures in RAN Integration

The proposed AI platform is adaptable, capable of fitting into open architectures that adhere to O-RAN standards as well as proprietary designs controlled by RAN vendors. This flexibility allows for a range of deployment scenarios, from a fully O-RAN compliant implementation that encourages third-party development to a fully proprietary model, or to a hybrid model that offers a balance between vendor control and innovation. In each scenario, the distributed AI platform can be customized to suit the specific needs of the infrastructure provider or adhere to the guidelines of standardization bodies.

Concluding Thoughts on AI’s Future in 6G RAN

The integration of AI into the RAN is central to the 6G vision, with the potential to transform network management, performance optimization, and application support. While deploying AI solutions in RAN presents challenges, a distributed AI-native platform offers a pathway to overcome these obstacles. By fostering discussions around the architecture of a 6G AI platform, we can guide standards bodies and vendors in exploring opportunities for AI integration. The proposed platform is intentionally flexible, allowing for customization to meet the diverse needs and constraints of different operators and vendors.

The future of RAN will depend on its ability to dynamically adapt to changing conditions and demands. AI is essential to this transformation, providing the intelligence and adaptability needed to manage the complexity of next-generation networks. As the industry progresses towards AI-native 6G networks, embracing both the challenges and opportunities that AI brings will be crucial. The proposed distributed AI platform marks a significant step forward, aiming to unlock the full potential of RAN through intelligent, flexible, and scalable solutions.

Innovation in AI and the commitment to an AI-native RAN are key to ensuring the telecommunications industry and the telecommunications networks of the future are efficient, cost-effective, and capable of supporting advanced services and applications. Collaborative efforts from researchers and industry experts will be vital in refining this vision and making the potential of AI in 6G RAN a reality.

As we approach the 6G era, integrating AI into RAN architectures is not merely an option but a necessity. The distributed AI platform outlined here serves as a blueprint for the future, where AI is seamlessly integrated into RAN, driving innovation and enhancing the capabilities of cellular networks to meet the demands of next-generation users and applications. 

For more details, please check the full paper.

Acknowledgements

The project is partially funded by the UK Department for Science, Innovation & Technology (DSIT) under Open Network Ecosystem Competition (ONE) programme.

​Microsoft Tech Community – Latest Blogs –Read More 

Hello,

I need help with a few things.

1. When someone fills out the form (on the right), it then populates into the list (on the left). When I comment on the populated row within the list, the user I am tagging is not getting notifications. How can I fix this? I do not want anyone else having access to the full list, just my comment.

2. Is there a way to create a workflow for approvals? I.e. if it’s a primary buyer I want these 3 people to approve in order, but if it’s an investor then I want these 4 people to approve in order?

​Hello, I need help with a few things. 1. When someone fills out the form (on the right), it then populates into the list (on the left). When I comment on the populated row within the list, the user I am tagging is not getting notifications. How can I fix this? I do not want anyone else having access to the full list, just my comment. 2. Is there a way to create a workflow for approvals? I.e. if it’s a primary buyer I want these 3 people to approve in order, but if it’s an investor then I want these 4 people to approve in order?    Read More

​

I am adding data sources to our collections in Purview so I can start doing scans for our Data Catalog.  I have added 5 data sources already and installed the Azure Arc extension and registered the data source as a SQL Server (Not arc enable just a regular SQL Server) and I have run some scans on those data sources.

Recently, I added another data source and just registered it as a regular SQL Server and skipped the Azure arc extension.

Do I have to install the extension for the scans to work?  Is the extension just for if I wanted to manage the SQL Servers, which are on Prem, in the Azure environment…which I don’t at this time.

Thank you

​I am adding data sources to our collections in Purview so I can start doing scans for our Data Catalog.  I have added 5 data sources already and installed the Azure Arc extension and registered the data source as a SQL Server (Not arc enable just a regular SQL Server) and I have run some scans on those data sources. Recently, I added another data source and just registered it as a regular SQL Server and skipped the Azure arc extension. Do I have to install the extension for the scans to work?  Is the extension just for if I wanted to manage the SQL Servers, which are on Prem, in the Azure environment…which I don’t at this time. Thank you  Read More

​

Hello Community, 

I am having issues integrating Jira with Sentinel. I have connected the logic app to Jira API. Now the logic app is not flowing. I receive a 406 error code during a step in the logic. I made sure the project was the same as listed in Jira. I even tried using the project key. 

​Hello Community,  I am having issues integrating Jira with Sentinel. I have connected the logic app to Jira API. Now the logic app is not flowing. I receive a 406 error code during a step in the logic. I made sure the project was the same as listed in Jira. I even tried using the project key.        Read More

​