Hello,

A former member of staff set up many different SharePoint notifications before they left the company.  Once they left their MS Office license was removed and their email was suspended.  SharePoint continues to send out notifications to this non-working email address, which is creating cases in our CRM.

How can I, as the System Admin, turn off these notifications?

Thank you

​Hello, A former member of staff set up many different SharePoint notifications before they left the company.  Once they left their MS Office license was removed and their email was suspended.  SharePoint continues to send out notifications to this non-working email address, which is creating cases in our CRM. How can I, as the System Admin, turn off these notifications? Thank you  Read More

​

I have a multiline text column (In a library, not a list) that contains text that has characters such as , { and } that I would like to remove. each, I want to be replaced with a line break and remove characters such as {}  or replace with a space. My code seems to only work on the first instance of these characters. I would love to get idea or suggestions as I know there are limitations around Multiline Text fields.

My Code:

{

  “$schema”: “https://developer.microsoft.com/json-schemas/sp/v2/column-formatting.schema.json“,

  “elmType”: “div”,

  “style”: {

    “white-space”: “pre-line”

  },

  “txtContent”: “=if(@currentField, replace(replace(replace(@currentField, ‘,’, ‘n’), ‘{‘, ”), ‘}’, ”), ”)”

}

​I have a multiline text column (In a library, not a list) that contains text that has characters such as , { and } that I would like to remove. each, I want to be replaced with a line break and remove characters such as {}  or replace with a space. My code seems to only work on the first instance of these characters. I would love to get idea or suggestions as I know there are limitations around Multiline Text fields.My Code:{  “$schema”: “https://developer.microsoft.com/json-schemas/sp/v2/column-formatting.schema.json”,  “elmType”: “div”,  “style”: {    “white-space”: “pre-line”  },  “txtContent”: “=if(@currentField, replace(replace(replace(@currentField, ‘,’, ‘n’), ‘{‘, ”), ‘}’, ”), ”)”}  Read More

​

Since Microsoft retired the Fundraising and Engagement app, they’ve introduced template apps for partners and customers to build solutions. Over the past year, we did lot of customizations on top of the original Fundraising and Engagement app, and we’re now considering moving these customizations and needed functionality to the ‘Fundraising’ template app. However, we’re concerned about long-term stability. Can Microsoft confirm if there’s any risk of these templates being retired or delisted? This would help us and others plan with greater confidence.

​Since Microsoft retired the Fundraising and Engagement app, they’ve introduced template apps for partners and customers to build solutions. Over the past year, we did lot of customizations on top of the original Fundraising and Engagement app, and we’re now considering moving these customizations and needed functionality to the ‘Fundraising’ template app. However, we’re concerned about long-term stability. Can Microsoft confirm if there’s any risk of these templates being retired or delisted? This would help us and others plan with greater confidence.  Read More

​

In Angular, I make API calls by subscribing to observables. When a user cancels a request, the application unsubscribes to the subscription which cancels the request (I can see this in the network tab of the Chrome browser). However, it does not trigger the endpoint and so the cancellation token param in our endpoint is of no use. Is there a way around this? I need the cancellation token to cancel all database request.

​In Angular, I make API calls by subscribing to observables. When a user cancels a request, the application unsubscribes to the subscription which cancels the request (I can see this in the network tab of the Chrome browser). However, it does not trigger the endpoint and so the cancellation token param in our endpoint is of no use. Is there a way around this? I need the cancellation token to cancel all database request.  Read More

​

For many IT admins, application delivery management is one of their most complex tasks. However, with the recent general availability of App attach in Azure Virtual Desktop, this process has become significantly easier. App attach simplifies application delivery administration by enabling dynamic application attachment from an application package to a user session in Azure Virtual Desktop. This solution has been gaining momentum, with numerous organizations embracing it to lower the burden on admins, while enabling granular, secured application assignment to employees.

As organizations continue their digital transformation, many admins find themselves managing on-premises applications for their existing virtual desktop infrastructure (VDI) environments even as they are moving their organizations to the cloud. As a result, admins must often use multiple tools for application delivery, creating a new layer of unwelcome complexity.

We are excited to announce that Liquidware, Numecent, and Omnissa are launch partners that have integrated their application delivery solutions with App attach in Azure Virtual Desktop. Integrating the solution with three of the top application delivery providers will offer substantial benefits to admins in organizations of all sizes. Additionally, the App attach in Azure Virtual Desktop API is publicly available, allowing providers to take advantage of this integration for enhanced application delivery.

The benefits of solution integration

Solution integration offers two significant benefits: simplified application delivery management and increased application mobility.

Simplified application delivery management

Admins can more easily run and deliver apps on Azure Virtual Desktop in the Microsoft Azure cloud thanks to a streamlined tracking and delivery system. This system offers a unified management console that provides a centralized point of control for all applications, simplifying the administrative workload. With this “single pane of glass” approach, administrators can efficiently track and manage apps from one place, no matter where those apps are deployed.

Increased application mobility

Migrating on-premises apps to Azure can be a daunting task. However, by leveraging the power of the integration of App attach in Azure Virtual Desktop with the app delivery solutions provided by leading partners, the process for admins to transfer their on-premises applications to Azure is streamlined. This helps address such common challenges as data transfer, synchronization, and maintaining security and compliance standards. The integration with Azure Virtual Desktop ensures that app delivery is consistent and efficient across all environments.

Application delivery partner benefits

Liquidware FlexApp containerizes applications into self-sufficient executables, enabling rapid deployment through various methods, including popular cloud file-sharing services. It is designed for use with any Windows desktop including cloud-hosted virtualization.

“FlexApp’s seamless integration with App attach in Azure Virtual Desktop empowers organizations to manage applications without adding complexity for end users. FlexApp helps customers achieve increased compatibility for attached apps. Our technology delivers apps that feel native while reducing IT overhead, making it a game changer for IT teams and end users alike.” – Jason E. Smith, Vice President, Alliances and Product Marketing, Liquidware

Numecent Cloudpager makes it easy to create, automate, and manage desktop application configurations from the cloud. Delivered as a fully managed software-as-a-service (SaaS) solution, users can leverage the latest Windows technologies to enhance their virtual desktop environments.

“Cloudpager enables seamless provisioning of complex legacy, custom, and resource-intensive apps to any Windows desktop, including non-persistent Azure Virtual Desktop environments, securely and compliantly. App attach in Azure Virtual Desktop integration provides customers with even more flexibility around deploying applications to Microsoft Azure.” – Arthur Hitomi, Co-founder and Chief Executive Officer, Numecent

Omnissa (formerly the end-user computing division of VMware) App Volumes is a real-time delivery and lifecycle management solution that provides faster app delivery and simplified and unified management of apps and users. Enterprises can use App Volumes to centrally manage applications that are deployed to desktops via virtual disks.

“Expanding our partnership with Microsoft to integrate App Volumes with App attach in Azure Virtual Desktop adds even more value for customers, streamlining app management and cutting costs. Our teams have worked together to deliver this integration, allowing organizations using Azure Virtual Desktop to optimize their environments through real-time app delivery using App Volumes with Apps on Demand.” – Bharath Rangarajan, Chief Product Officer, Omnissa

Unlock new possibilities

App attach in Azure Virtual Desktop simplifies on-premises administration and application migration to the cloud by integrating seamlessly with third-party solutions, enhancing the overall application delivery experience. This integration provides increased security, scalability, and flexibility, ensuring a unified and efficient application delivery process across all environments. Microsoft is actively collaborating with partners to add more integrations, further improving the versatility and effectiveness of application delivery solutions.

To learn more about the third-party integration for app attach, visit the App attach documentation.

Stay up to date! Bookmark the Azure Virtual Desktop Tech Community.

​Microsoft Tech Community – Latest Blogs –Read More 

We previously announced that support would end for retired Azure classic storage accounts on 31 August 2024. Now that we are past the retirement date and Azure classic storage accounts are not supported, we have some updates to share on our plans and the considerations for customers who have not migrated their remaining classic storage accounts to Azure Resource Manager.

On or after 1 November 2024:

Your ability to perform write operations using the classic service model APIs, including PUT and PATCH, will be limited. You will only be able to perform read and list operations using the classic service model APIs.
Your remaining classic storage accounts will be migrated to Azure Resource Manager on your behalf on a rolling schedule. Your data will continue to be stored, but any applications that use the classic service model APIs to perform management plane operations will experience disruptions if you’re actively using any write operations. Write operations will only be available through the Azure Resource Manager APIs after your account(s) have been migrated. 

Note: There are no impacts on the availability of the data plane APIs before, during, or after the migration of classic storage accounts.

Azure storage accounts under Azure Resource Manager provides the same capabilities as well as new features, including:

A management layer that simplifies deployment by enabling you to create, update, and delete resources.
Resource grouping, which allows you to deploy, monitor, manage, and apply access control policies to resources as a group.

To avoid service disruptions, you’ll need to migrate your classic storage accounts to Azure Resource Manager as soon as possible. Our recommendation is that customers self-service their migrations using the existing migration capabilities instead of waiting for Azure Storage to migrate any remaining classic storage accounts on your behalf. This ensures that you can migrate on your schedule and minimize impacts. We cannot guarantee that you will not experience service interruptions if you further delay your migration.

Required action

To avoid service disruptions, migrate your classic storage accounts as soon as possible. Additionally, update any management operations in code or applications that target the classic deployment model. Read our FAQ and migration guidance for more information.

Help and support

If you have questions, get answers from community experts in Microsoft Q&A. If you have a support plan and you need technical help, open the Azure portal and select the question mark icon at the top of the page.

FAQ

Q: I want to delete my account, but the operation is blocked. How do I delete my account if write operations are blocked on the classic control plane? 
A: You can delete your account using the Azure Resource Manager APIs for Azure Storage after your account has been migrated from the classic service model.

Q: Will I be able to use the legacy PowerShell cmdlets to manage my classic storage accounts?
A: No. After your accounts have been migrated, they can be managed with the modern PowerShell cmdlets for Azure Storage and Azure Resource Manager. 

Q: What resource group will my migrated resources appear in? 

A: Any classic storage accounts that are migrated on your behalf will be placed in a new resource group. The name of the new resource group follows the pattern <classic-account-name>-Migrated. For more information on the migration process, visit Understand storage account migration from the classic deployment model to Azure Resource Manager. 

​Microsoft Tech Community – Latest Blogs –Read More 

The following happened to a user. On Friday, October 18th, at around 9 p.m., he automatically sent out an older recurring appointment in Outlook on behalf of another person. What could be the cause and how can I identify this problem?

​The following happened to a user. On Friday, October 18th, at around 9 p.m., he automatically sent out an older recurring appointment in Outlook on behalf of another person. What could be the cause and how can I identify this problem?  Read More

​

kenapa saya tidak bisa memulai modul tersebut, tidak ada tombol biru dengan bacaan mulai di dalam modul tersebut, padahal saya sudah menyelesaikan modul sebelumnya sebagai prasyarat untuk memulai modul tersebut. beriku ini modul yang mengalami masalah

https://learn.microsoft.com/id-id/training/modules/create-azure-ai-custom-skill/

​kenapa saya tidak bisa memulai modul tersebut, tidak ada tombol biru dengan bacaan mulai di dalam modul tersebut, padahal saya sudah menyelesaikan modul sebelumnya sebagai prasyarat untuk memulai modul tersebut. beriku ini modul yang mengalami masalahhttps://learn.microsoft.com/id-id/training/modules/create-azure-ai-custom-skill/  Read More

​

The Future of AI: LLM Distillation just got easier

Part 4 – Maximize your fine-tuned model performance with the new Azure AI Evaluation SDK

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.

Generated using Azure OpenAI DALL-E 3

In earlier posts of this distillation series, we detailed the process of distilling a Llama 3.1 405B model into a more compact Llama 3.1 8B model. This journey included generating a synthetic dataset using RAFT, as well as fine-tuning and deploying our student model on Azure AI Serverless.

But how can we confirm that our distilled model performs optimally? The crucial final step is evaluating the model.

Effective model evaluation is key to ensuring that our AI systems function as expected and meet the desired standards. With the introduction of the Azure AI Evaluation Python SDK, we now have a powerful toolkit for assessing AI models through advanced metrics. In this blog post, we’ll look at evaluating a distilled student model, which was trained with data generated by RAFT, and compare it against a baseline model.

In our setup, Llama 3.1 405B functions as the teacher, Llama 3.1 8B serves as the student model and GPT-4 serves as the judge.

Why evaluate?

Evaluating distilled student models is crucial because it allows us to assess how effectively knowledge has been transferred from the teacher model to the student model. Distillation aims to compress a larger, more complex model into a smaller, more efficient one without significantly sacrificing performance. By thoroughly evaluating the distilled models, we ensure they not only mimic the teacher model’s outputs but also maintain high levels of accuracy, coherence, and relevance. This evaluation process helps identify areas where the student model may need further fine-tuning and ensures that the distilled models are ready for deployment in resource-constrained environments where computational efficiency is paramount.

Process Overview

Evaluating the performance of our models involves several key steps, which can be broadly categorized under Testing and Scoring.

Testing

Run the Baseline Model on the Evaluation Split: Our first step is to run the teacher model (Llama 3.1 405B) on the evaluation split to generate its predictions.
Run the Student Model on the Evaluation Split: Next, we run the student model on the same evaluation dataset to generate its predictions.

Scoring

Calculate Metrics for the Baseline Model: Using the predictions from the baseline model, we calculate various performance metrics.
Calculate Metrics for the Student Model: Similarly, we calculate the performance metrics for the student model’s predictions.
Compare Metrics: Finally, we compare the performance of both models, highlighting the results through visuals and diagrams.

Testing the baseline and student models

Installing the SDK

First, you need to install the Azure AI Evaluation SDK:

pip install openai azure-ai-evaluation azure-identity promptflow-azure

Note on SDK Availability: It’s important to highlight that the Azure AI Evaluation SDK is currently in beta. This means that while the SDK offers a comprehensive suite of tools and features for evaluating AI models, it may still undergo changes and improvements. Users should stay updated with any modifications or enhancements introduced by Azure, and consider providing feedback to help refine and optimize the SDK for wider use in its official release.

Baseline Model Testing

This will generate answers to the questions in the evaluation dataset using the baseline model:

env $(cat .env .env.state) python .gorilla/raft/eval.py
–question-file $dataset_path_hf_eval
–answer-file $dataset_path_hf_eval_answer_baseline
–model $BASELINE_OPENAI_DEPLOYMENT
–env-prefix BASELINE
–mode $BASELINE_MODEL_API

Note: JSONL file format needs to be further converted to a format suitable for testing, see eval notebook for details.

Student Model Testing

This will generate answers to the questions in the evaluation dataset using the student model:

env $(cat .env .env.state) python .gorilla/raft/eval.py
–question-file $dataset_path_hf_eval
–answer-file $dataset_path_hf_eval_answer
–model $STUDENT_DEPLOYMENT_NAME
–env-prefix STUDENT
–mode $STUDENT_MODEL_API

Note: JSONL file format needs to be further converted to a format suitable for testing, see eval notebook for details.

Let’s look at a sample

This sample is extracted from the evaluation split and shows the baseline and student answers:

question: What types of waves do strong direct offshore winds create?

gold_final_answer: plunging or large barrel waves

context <DOCUMENT>Lefts, Rights, and A-frames could be directed from this pump design providing forWave intensity Artificial reefs Artificial wavesSurfing a stationary, artificialwave in Southern California A surfer going for the tube Catching waves at a surfing conteston the North Shore of Oahu, Hawaiirippable surf and barrel rides. The Ocean Dome cost about $2 billion tobuild and was expensive to maintain.[31] The Ocean Dome was closed in2007.</DOCUMENT> <DOCUMENT>However, thewaves that are produced by reef breaks are some of the best in the world. Famous reef breaks arepresent in Padang Padang (Indonesia), Pipeline (Hawaii), Uluwatu (Bali), and Teahupo’o(Tahiti).[49][52]A ledge break is formed by steep rocks ledges that make intense waves because the waves travelthrough deeper water then abruptly reach shallower water at the ledge. Shark Island, Australia is alocation with a ledge break.</DOCUMENT>

baseline_answer: Strong direct offshore winds create plunging or large barrel waves. These waves are characterized by their increased height and intensity due to the shallow water depth when they break.

student_answer: plunging or large barrel waves

This sample was chosen randomly and in this case, the student model answer is identical to the gold answer. This is not always the case.

Evaluating the baseline and student model responses

Built-in Evaluators

The Azure AI Evaluation SDK offers an extensive suite of built-in metrics, designed to facilitate comprehensive evaluation of AI models. In the following sections, we’ll highlight selected evaluators and provide detailed examples of their application, showcasing how they can enhance your model assessments.

They are categorized into two main groups: (1) metrics that leverage GPT models for scoring, providing advanced qualitative assessments, and (2) metrics that utilize straightforward mathematical calculations for evaluation.

GPT based metrics

Category

Evaluator Class

Notes

Quality

GroundednessEvaluator

Groundedness measures the extent to which the generated content is based on factual correctness and aligns with the provided data or context.

RelevanceEvaluator

Relevance assesses how pertinent the generated text is to the given input or prompt. Higher relevance scores indicate that the generated responses are more appropriate and closely aligned with the query or topic.

CoherenceEvaluator

Coherence measures how logically consistent and semantically meaningful the generated text is. Higher coherence indicates better understanding and logical consistency.

FluencyEvaluator

Fluency evaluates how naturally the generated text reads. Fluent text should be grammatically correct and smooth in its flow.

SimilarityEvaluator

Measures the similarity between the predicted answer and the correct answer

Content Safety

ViolenceEvaluator

SexualEvaluator

SelfHarmEvaluator

HateUnfairnessEvaluator

Composite

QAEvaluator

Built on top of individual quality evaluators.

ChatEvaluator

Similar to QAEvaluator but designed for evaluating chat messages.

ContentSafetyEvaluator

Built on top of individual content safety evaluators.

Math based metrics

Evaluator Class

Notes

BleuScoreEvaluator

BLEU (Bilingual Evaluation Understudy) is a widely-used metric for evaluating the quality of text generated by an AI by comparing it to one or more reference texts. It particularly looks at the precision of n-grams in the generated text.

RougeScoreEvaluator

ROUGE (Recall-Oriented Understudy for Gisting Evaluation) primarily measures recall, comparing n-grams between the generated text and reference texts. It is commonly used for evaluation in summarization tasks.

F1ScoreEvaluator

A balance between precision and recall, the F1 score provides a single metric that combines both, offering a more comprehensive view of performance in classification problems.

Running metrics individually

The Azure AI Evaluation SDK enables the utilization of individual metrics. This feature is particularly useful for experimentation, gaining deeper insights, and incorporating metrics into bespoke evaluation workflows.

Tech Tip: This blog post is crafted using the Quarto writing system, a versatile tool for publishing with code. The Azure AI Evaluation metrics are seamlessly executed and displayed inline within this post.

Let’s look first at the F1 Score math metric

For a response that is accurate but includes additional information not found in the ground truth:

from azure.ai.evaluation import F1ScoreEvaluator
f1_score_evaluator = F1ScoreEvaluator()
f1_score = f1_score_evaluator(
ground_truth=”The capital of Japan is Tokyo.”,
response=”Tokyo is Japan’s capital, known for its blend of traditional culture”
)
print(f”The F1 Score is {round(f1_score[‘f1_score’], 2)}”)

The F1 Score is 0.5

For a response that is accurate but uses the same words turned differently:

from azure.ai.evaluation import F1ScoreEvaluator
f1_score_evaluator = F1ScoreEvaluator()
f1_score = f1_score_evaluator(
ground_truth=”The capital of Japan is Tokyo.”,
response=”Tokyo is Japan’s capital”
)
print(f”The F1 Score is {round(f1_score[‘f1_score’], 2)}”)

The F1 Score is 0.67

Let’s look first at the Similarity GPT metric

We first need to instantiate the Judge model client:

from os import getenv
from azure.ai.evaluation import AzureOpenAIModelConfiguration

model_config = AzureOpenAIModelConfiguration(
azure_endpoint = getenv(“JUDGE_AZURE_OPENAI_ENDPOINT”),
azure_deployment = getenv(“JUDGE_AZURE_OPENAI_DEPLOYMENT”),
api_version = getenv(“JUDGE_OPENAI_API_VERSION”),
)

Let’s now instantiate the Similarity score metric:

from azure.ai.evaluation import SimilarityEvaluator
similarity_evaluator = SimilarityEvaluator(model_config)

For a response that is accurate but includes additional information not found in the ground truth:

similarity = similarity_evaluator(
query=”What’s the capital of Japan?”,
ground_truth=”The capital of Japan is Tokyo.”,
response=”Tokyo is Japan’s capital, known for its blend of traditional culture”
)
print(f”The Similarity is {similarity[‘gpt_similarity’]}”)

The Similarity is 4.0

For a response that is accurate but uses the same words turned differently:

similarity = similarity_evaluator(
query=”What’s the capital of Japan?”,
ground_truth=”The capital of Japan is Tokyo.”,
response=”Tokyo is Japan’s capital”
)
print(f”The Similarity is {similarity[‘gpt_similarity’]}”)

The Similarity is 5.0

GPT-based similarity metrics demonstrate greater robustness in evaluating correct responses that are phrased differently compared to traditional F1 Scores.

Running metrics in bulk

While evaluating metrics individually helps in understanding their functionality, acquiring statistically significant results necessitates running them on a larger scale across an evaluation dataset.

The Azure AI Evaluation SDK provides a convenient bulk evaluation capability via the evaluate function.

To begin, we need to initialize the evaluators that will be used to assess the student and baseline models:

from azure.ai.evaluation import CoherenceEvaluator, F1ScoreEvaluator, FluencyEvaluator, GroundednessEvaluator, RelevanceEvaluator, SimilarityEvaluator, BleuScoreEvaluator, RougeScoreEvaluator, RougeType

# Initializing evaluators
evaluators = {
# GPT based metrics
“coherence” : CoherenceEvaluator(model_config),
“f1_score” : F1ScoreEvaluator(),
“fluency” : FluencyEvaluator(model_config),
“groundedness” : GroundednessEvaluator(model_config),
“relevance” : RelevanceEvaluator(model_config),
“similarity” : SimilarityEvaluator(model_config),

# Math metrics
“bleu” : BleuScoreEvaluator(),
“rouge_1” : RougeScoreEvaluator(RougeType.ROUGE_1),
“rouge_2” : RougeScoreEvaluator(RougeType.ROUGE_2),
}

Note that we have previously executed the baseline and student models on the evaluation dataset, which means the JSONL file provided to the evaluate function already includes their responses. Consequently, further model invocations are unnecessary at this stage.

Recommendation: It’s often beneficial to run the baseline and student models once initially. By doing so, you can execute the evaluate function multiple times with various metrics configurations without re-incurring the inference time and costs associated with model executions. Note that while this avoids repeated inference expenses, using GPT-based metrics will still incur costs and time for each evaluate execution, as the Judge model is utilized.

from azure.ai.evaluation import evaluate

result = evaluate(
data=”test-results-[baseline|student].jsonl”,
evaluators=evaluators,
evaluator_config={
“default”: {
“column_mapping”: {
“query”: “${data.question}”,
“response”: “${data.final_answer}”,
“ground_truth”: “${data.gold_final_answer}”,
“context”: “${data.context}”,
}
}
},
)

This command initiates a background process that hosts a user interface locally. Here is an example of its appearance:

The interface updates in real-time to display the progress of the scoring process on the evaluation dataset.

Additionally, you can click on each completed line to view the detailed trace of the calls. This feature is particularly useful for GPT-based metrics, as it reveals the system prompt used and provides insights into the underlying logic that contributed to the final score:

Comparing Metrics and Visualizing Results

Note: You can find the implementation details for generating the comparison figures of baseline and student metrics in the repository notebook. This resource provides comprehensive insights into how the metric comparisons were conducted, along with the code necessary to reproduce these visualizations.

Going further with continuous model evaluation and GenAIOps

This marks the beginning of a continuous improvement journey. It’s quite common to find that the student model’s initial performance does not meet expectations. Through our evaluation, we may uncover areas needing adjustment—whether it’s refining the synthetically generated dataset, optimizing fine-tuning parameters, or other elements. This initiates a cycle of iterative improvement and reassessment before the model is ready for deployment in production.

To effectively help you navigate this process, we came up with the GenAIOps Maturity Model, which serves as a comprehensive guide for evaluating your progress and maturity in operationalizing AI models.

Conclusion

By leveraging the Azure AI Evaluation Python SDK, we gain a detailed understanding of how our distilled student model compares to the baseline model across a spectrum of performance indicators. This structured evaluation framework not only helps in refining our models but also ensures that we are continuously improving and delivering robust AI solutions.

Explore, fork and clone the comprehensive 🚀🔥 GitHub Recipe Repository for complete code coverage on executing the full distillation process, including in-depth evaluations as detailed in this blog post. Discover step-by-step notebooks and resources to master the entire pipeline efficiently.

Stay tuned for more insights and tutorials on advanced AI topics and the latest tools available in the Azure ecosystem!

​Microsoft Tech Community – Latest Blogs –Read More 

Good day.

I created a Power Automate flow that takes a date and time and populates them into an Excel spreadsheet. However, once imported, the date and time are recognized by Excel as text, not as date and time values. This persists until I select the cell, place my cursor in the formula bar, and press Enter. Only then are they recognized as date and time. I am using formatDateTime in Power Automate to export the date and time.

Any help would be appreciated.

​Good day.I created a Power Automate flow that takes a date and time and populates them into an Excel spreadsheet. However, once imported, the date and time are recognized by Excel as text, not as date and time values. This persists until I select the cell, place my cursor in the formula bar, and press Enter. Only then are they recognized as date and time. I am using formatDateTime in Power Automate to export the date and time.Any help would be appreciated.  Read More

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