Hello All – I have a scenario and wanted to know how I can use the booking app.

We have to set up a meeting with a panel of 4 Senior Leadership Members and 30 different teams. They panel would talk to the team / Users individually. I am looking at getting schedules that are commonly available for all 4 SLT members and have that published in Bookings app so that the teams can block the time slots available. Once the slot is booked, it should put that on the calendar of the user and the panel based on the subject chosen by the users.  

In case a slot is taken by Team 1, they would have only 29 others open and so on. How can I achieve this. 

​Hello All – I have a scenario and wanted to know how I can use the booking app. We have to set up a meeting with a panel of 4 Senior Leadership Members and 30 different teams. They panel would talk to the team / Users individually. I am looking at getting schedules that are commonly available for all 4 SLT members and have that published in Bookings app so that the teams can block the time slots available. Once the slot is booked, it should put that on the calendar of the user and the panel based on the subject chosen by the users.   In case a slot is taken by Team 1, they would have only 29 others open and so on. How can I achieve this.   Read More

​

Due to previous mistake. I only allocated 80GB storage for Windows OS. Now, there is only 10GB free space left in my C drive as Windows 11 eats up more space than Windows 10. In fact, there is a plenty of free space available in D drive (nearly 100GB). Is there any to how to increase c drive space in windows 11 without formatting the drive?

I don’t want to reinstall Windows 11 as it is really a time consuming task. Kindly share your experience if you knew how to add more space to C drive in Windows 11 without formatting. 

P.S. there is no option to extend C drive in Windows 11 when opening the Disk Management app.

​Due to previous mistake. I only allocated 80GB storage for Windows OS. Now, there is only 10GB free space left in my C drive as Windows 11 eats up more space than Windows 10. In fact, there is a plenty of free space available in D drive (nearly 100GB). Is there any to how to increase c drive space in windows 11 without formatting the drive? I don’t want to reinstall Windows 11 as it is really a time consuming task. Kindly share your experience if you knew how to add more space to C drive in Windows 11 without formatting.  P.S. there is no option to extend C drive in Windows 11 when opening the Disk Management app.  Read More

​

I’m facing issue while using office 365 version 2408 for document to send to Docusign . Error is “unable to convert to pdf”. But same issue is not there if I use older office 365 version 2308 or earlier 

​I’m facing issue while using office 365 version 2408 for document to send to Docusign . Error is “unable to convert to pdf”. But same issue is not there if I use older office 365 version 2308 or earlier   Read More

​

Hello

Is it possible to define templates that includes the complete tree?
Epic, Features, Stories.

Regards
JFM_12

​HelloIs it possible to define templates that includes the complete tree?Epic, Features, Stories.RegardsJFM_12  Read More

​

Hi,

I really need some help in here as I just upgraded my PC to Windows 11 from Windows 10. I have more than 10 .webp images downloaded from web and currently look for a way to bulk convert webp to png so I can edit and share them with others.

Does Windows 11 comes with a WebP to PNG Converter? If yes, could you kindly let me know. In addition, it could be better to keep the quality after conversion.

Thank you

​Hi,I really need some help in here as I just upgraded my PC to Windows 11 from Windows 10. I have more than 10 .webp images downloaded from web and currently look for a way to bulk convert webp to png so I can edit and share them with others. Does Windows 11 comes with a WebP to PNG Converter? If yes, could you kindly let me know. In addition, it could be better to keep the quality after conversion. Thank you   Read More

​

Before diving in, let’s take a moment to review the key resources and foundational concepts that will guide us through this blog. That will ensure we’re well-equipped to follow along. This brief review will provide a strong starting point for exploring the main topics ahead.

Microsoft Azure: Microsoft offers a cloud computing platform and a suite of cloud services. It provides a wide range of cloud-based
services and solutions that enable organizations to build, deploy, and manage applications and services through Microsoft’s global network of data centers. 
AI Studio: a platform that helps you evaluate model responses and orchestrate prompt application components with prompt flow for better performance. The platform facilitates scalability for transforming proof of concepts into full-fledged production with ease, continuous monitoring and refinement support long-term success. 

Fine-tuning: is the process of retraining pretrained models on specific datasets. The purpose is typically to improve model performance on specific tasks or to introduce information that wasn’t well represented when you originally trained the base model.

Retrieval Augmented Generation (RAG): is a pattern that works with pretrained large language models (LLM) and your own data to generate responses. In Azure Machine Learning, you can implement RAG in a prompt flow.

Our hands-on learning will be developing an AI-based solution that helps the user extract financial information and insights from investment/finance books and newspaper in our database.

The process is divided into three main parts:

Fine-tune a base model with financial data to help the model provide more specific responses and be grounded and rooted with data related to finance and investment.
Implement RAG so that the response won’t be only based on the data it was trained with (fine-tuned with) but also based on other data sources (the user’s input in our case).
Integration of the deployed model into a web app so that it could be used through a user interface.

1- Setup:

Create a resource group which is defined as a container that holds related resources for an Azure solution. The resource group can include all the resources for the solution, or only those resources that you want to manage as a group.
You need to specify your subscription, a unique resource group name, and the region.
Create an Azure OpenAI resource: Azure OpenAI Service provides REST API access to OpenAI’s powerful language models including GPT-4o, GPT-4 Turbo with Vision, GPT-4, GPT-3.5-Turbo, and Embeddings model series. These models can be easily adapted to your specific task including but not limited to content generation, summarization, image understanding, semantic search, and natural language to code translation

– Create a text embedding model: the embedding is an information-dense representation of the semantic meaning of a piece of text. Each embedding is a vector of floating-point numbers, such that the distance between two embeddings in the vector space is correlated with semantic similarity between two inputs in the original format. 
Create an AI search resource: Azure AI Search (“Azure Cognitive Search” previously) provides secure information retrieval at scale over user-owned content in traditional and generative AI search applications. Information retrieval is foundational to any app that surfaces text and vectors. Common scenarios include data exploration, and increasingly feeding query results to prompts based on your proprietary grounding data for conversational search as we will do in our example.

Create a storage account: it contains all your Azure Storage data objects: blobs, files, queues, and tables. The storage account provides a unique namespace for your Azure Storage data that is accessible from anywhere in the world over HTTP or HTTPS.

– Create a blob container: blob Storage is Microsoft’s object optimized for storing massive amounts of unstructured data. Unstructured data is data that doesn’t adhere to a particular data model or definition, such as text or binary data. it will be used to store your data.
Navigate to your storage resource -> Click on Storage browser tab on the left -> Click Blob Containers -> Click on + add container then Upload your data. Our data was pdf files (books and newspapers) and csv files from Kaggle, all are related to finance and investment.

Create a search Index: is your searchable content, available to the search engine for indexing, full text search, vector search, hybrid search, and filtered queries. Check that the status of your AI search is “Running”.
– Import and vectorize data: integrated vectorization is an extension of the indexing and query pipelines in Azure AI Search. It adds the following capabilities: Data chunking (split the data into smaller, manageable pieces) during indexing. Text-to-vector conversion during indexing.
Navigate to you AI search service -> Click on Indexes tab on the left -> Click on “Import and Vectorize data” -> Select your text embedding model previously deployed.

2- Fine Tune a Base Model

Preparing Dataset: before starting fine tuning, we need to prepare our training and validation data. Which could be collected manually or generated from text files or tabular data. However, it is important that the data confirm the following template:

{“messages”: [{“role”: “system”, “content”: “content goes here”}, {“role”: “user”, “content”: “query goes here?”}, {“role”: “assistant”, “content”: “response goes here.”}]}.

To satisfy this, we prepared our two data sets training_data.jsonl and val_data.jsonl for training and validation, respectively.

Below is an example of our training_data.jsonl:

{“messages”: [{“role”: “system”, “content”: “You are a helper in finance and an advisor in investment”}, {“role”: “user”, “content”: “What makes a successful startup?”}, {“role”: “assistant”, “content”: “Luck, hard work, and maybe skills… mostly luck, hard work, and consistency.”}]}

Both data files are attached to this blog. They were collected manually through some examples.

Evaluate data to ensure its quality, check number of tokens and its distribution.
import json
import tiktoken
import numpy as np
from collections import defaultdict

encoding = tiktoken.get_encoding(“cl100k_base”)

def num_tokens_from_messages(messages, tokens_per_message=3, tokens_per_name=1):
num_tokens = 0
for message in messages:
if not isinstance(message, dict):
print(f”Unexpected message format: {message}”)
continue
num_tokens += tokens_per_message
for key, value in message.items():
if not isinstance(value, str):
print(f”Unexpected value type for key ‘{key}’: {value}”)
continue
num_tokens += len(encoding.encode(value))
if key == “name”:
num_tokens += tokens_per_name
num_tokens += 3
return num_tokens

def num_assistant_tokens_from_messages(messages):
num_tokens = 0
for message in messages:
if not isinstance(message, dict):
print(f”Unexpected message format: {message}”)
continue
if message.get(“role”) == “assistant”:
content = message.get(“content”, “”)
if not isinstance(content, str):
print(f”Unexpected content type: {content}”)
continue
num_tokens += len(encoding.encode(content))
return num_tokens

def print_distribution(values, name):
if values:
print(f”n#### Distribution of {name}:”)
print(f”min / max: {min(values)}, {max(values)}”)
print(f”mean / median: {np.mean(values)}, {np.median(values)}”)
print(f”p5 / p95: {np.quantile(values, 0.05)}, {np.quantile(values, 0.95)}”)
else:
print(f”No values to display for {name}”)

files = [
r’train_data.jsonl’,
r’val_data.jsonl’
]

for file in files:
print(f”Processing file: {file}”)
try:
with open(file, ‘r’, encoding=’utf-8′) as f:
total_tokens = []
assistant_tokens = []
for line in f:
try:
ex = json.loads(line)
messages = ex.get(“messages”, [])
if not isinstance(messages, list):
raise ValueError(“The ‘messages’ field should be a list.”)
total_tokens.append(num_tokens_from_messages(messages))
assistant_tokens.append(num_assistant_tokens_from_messages(messages))
except json.JSONDecodeError:
print(f”Error decoding JSON line: {line}”)
except ValueError as ve:
print(f”ValueError: {ve} – line: {line}”)
except Exception as e:
print(f”Unexpected error processing line: {e} – line: {line}”)

if total_tokens and assistant_tokens:
print_distribution(total_tokens, “total tokens”)
print_distribution(assistant_tokens, “assistant tokens”)
else:
print(“No valid data to process.”)

print(‘*’ * 50)

except FileNotFoundError:
print(f”File not found: {file}”)
except Exception as e:
print(f”An unexpected error occurred: {e}”)
​
Login to AI Studio
Navigate to the Fine-tuning tab
Check the available models for fine-tuning within your region.

Upload your training and validation data

Since we have our data locally, we uploaded them. In case you want to save your data in the cloud and use the URL for later in place of the “Uploading files” option, you can use SDK and follow this code:

# Initialize AzureOpenAI client

client = AzureOpenAI(

azure_endpoint=azure_oai_endpoint,

api_key=azure_oai_key,

api_version=version # Ensure this API version is correct

)

training_file_name = r’path’

validation_file_name = r’path’

try:

# Upload the training dataset file

with open(training_file_name, “rb”) as file:

training_response = client.files.create(

file=file, purpose=”fine-tune”

)

training_file_id = training_response.id

print(“Training file ID:”, training_file_id)

except Exception as e:

print(f”Error uploading training file: {e}”)

try:

# Upload the validation dataset file

with open(validation_file_name, “rb”) as file:

validation_response = client.files.create(

file=file, purpose=”fine-tune”

)

validation_file_id = validation_response.id

print(“Validation file ID:”, validation_file_id)

except Exception as e:

print(f”Error uploading validation file: {e}”)

You can specify the hyperparameters such as batch size, or leave them with default values.
Review the settings before submitting

Check the status of the fine-tuning in your dashboard, changing from Queued to Running to Completed.
Once completed, your fine-tuned model is ready to be deployed. Click on ‘Deploy’
After successful deployment, you can go back to Azure Open AI and find your fine-tuned model deployed along with your previous text embedding model.

3- Integration into Web App

The concept here is to rely on the model’s knowledge + users’ documentation. We have two options and both provide high precision for responses:

Look for the answer in the documents, and if not found, return a response based on the internal knowledge of the model. 
Combine the two responses from the retriever and the model. Which is the one we opt for here.

Also, for integration, we have two ways we may follow: through the Azure OpenAI User Interface and deploying into an Azure static web app or develop your own web app and use the Azure SDK to integrate your model.

1- Deploying into Azure static web app

Click on “Open in Playground” below your deployments list in Azure open AI
Click “Add your data”
Choose your Azure blob storage as data source à Choose Index name “myindex”
Customize the system message to “You are a financial advisor and an expert in investment. You have access to a wide variety of documents. Use your own knowledge to answer the question and verify it or supplement it using the relevant documents when possible.” This system message will enable the model not only to rely on documents but also rely on its internal knowledge.
Complete the setup and click on “Apply changes”

Deploy to a new web app and configure the web app name, subscription, resource group, location, and pricing plan.

2- Develop your own web App and use Azure SDK

Prepare your environment
load_dotenv ()

azure_oai_endpoint = os.getenv(“AZURE_OAI_FINETUNE_ENDPOINT2”)

azure_oai_key = os.getenv(“AZURE_OAI_FINETUNE_KEY2”)

azure_oai_deployment = os.getenv(“AZURE_OAI_FINETUNE_DEPLOYMENT2”)

azure_search_endpoint = os.getenv(“AZURE_SEARCH_ENDPOINT”)

azure_search_key = os.getenv(“AZURE_SEARCH_KEY”)

azure_search_index = os.getenv(“AZURE_SEARCH_INDEX”)
​
Initialize your AzureOpenAI client

client = AzureOpenAI(

base_url=f”{azure_oai_endpoint}/openai/deployments/{azure_oai_deployment}/extensions”,

api_key=azure_oai_key,

api_version=”2023-09-01-preview)

Configure your data source for Azure AI search. This will retrieve response from our stored files.  

extension_config = dict(

dataSources= [

{

“type”: “AzureCognitiveSearch”,

“parameters”: {

“endpoint”: azure_search_endpoint,

“key”: azure_search_key,

“indexName”: azure_search_index,

}

}

]

)

Next, you can look at how to do real-time injection so that you personalize more of the responses. Try to find how to rely between your web app, the user’s input I/O, the searching index, and LLM.
Keyword: Langchain, Databricks

Resources:

what-is-azure-used-for.
What is Azure AI Studio? – Azure AI Studio | Microsoft Learn.  
Fine-tuning in Azure AI Studio – Azure AI Studio | Microsoft Learn.  
machine-learning/concept-retrieval-augmented-generation.  
Manage resource groups – Azure portal – Azure Resource Manager | Microsoft Learn.
What is Azure OpenAI Service? – Azure AI services | Microsoft Learn
Introduction to Azure AI Search – Azure AI Search | Microsoft Learn
storage-account-create
Introduction to Blob (object) Storage – Azure Storage | Microsoft Learn
How to generate embeddings with Azure OpenAI Service – Azure OpenAI | Microsoft Learn.
Azure OpenAI Service models – Azure OpenAI | Microsoft Learn
Search index overview – Azure AI Search | Microsoft Learn
Integrated vectorization – Azure AI Search | Microsoft Learn
Easy Guide to Transitioning from OpenAI to Azure OpenAI: Step-by-Step Process
LangChain on Azure Databricks for LLM development – Azure Databricks | Microsoft Learn
Build a RAG-based copilot solution with your own data using Azure AI Studio – Training | Microsoft Learn
RAG and generative AI – Azure AI Search | Microsoft Learn
Retrieval augmented generation in Azure AI Studio – Azure AI Studio | Microsoft Learn
Retrieval Augmented Generation using Azure Machine Learning prompt flow (preview) – Azure Machine Learning | Microsoft Learn
Retrieval-Augmented Generation (RAG) with Azure AI Document Intelligence – Azure AI services | Microsoft Learn

​Microsoft Tech Community – Latest Blogs –Read More 

Icons are not showing in MS Project online

​ Icons are not showing in MS Project online  Read More

​

Hi Folks,

we use some 3rd-party web-applications that send mails to exchange online via a relay-service. These mails carry a valid dkim-signature for our primary domain (d=…). Sender address is our accepted (primary) maildomain.

Exchange Online marks these mails as outside of the organisation, as they are neither authenticated via sending-IP, nor certificate. As the relay-service is a shared service, we do not want to whitelist the ip or ssl-cert as others use this system as well.

Is there a way to use the dkim-signature to tread this mails as internal?

Thank you.

Siegmar

​Hi Folks, we use some 3rd-party web-applications that send mails to exchange online via a relay-service. These mails carry a valid dkim-signature for our primary domain (d=…). Sender address is our accepted (primary) maildomain.Exchange Online marks these mails as outside of the organisation, as they are neither authenticated via sending-IP, nor certificate. As the relay-service is a shared service, we do not want to whitelist the ip or ssl-cert as others use this system as well. Is there a way to use the dkim-signature to tread this mails as internal? Thank you. Siegmar  Read More

​

Meet Somesh Pathak, a Security MVP from the Netherlands, whose journey took an unexpected turn when he was diagnosed with early-onset Parkinson’s in 2020. From battling the stigma at work to finding strength in his family, Somesh’s story is one of resilience and determination. We asked him how this challenge has reshaped his journey as a professional, community leader, and father.

How has being diagnosed with early-onset Parkinson’s affected both your personal and professional life?

I was diagnosed with Young-onset Parkinson’s disease (YOPD) back in 2020, but  early signs of hypokinetic rigid syndrome started in 2016–2017. I was living in India at the time and the doctors we’re not able to find the reason of my symptoms. I relocated to Stockholm in 2019 and underwent extensive tests at the Karolinska Hospital where I was diagnosed with idiopathic Parkinson’s disease.

Transformation from a healthy life to a life with a hidden disability has seriously affected my personal and professional life. One of the most difficult challenges I faced professionally was the lack of support from my previous manager at a former employer. At one point, I felt ashamed of having YOPD, primarily due to my symptoms and the stigma at work. Instead of receiving the assistance I needed, I was asked to work extended hours, which only stressed the situation.

Controlling the illness has also been quite challenging. One of the most difficult things I’ve experienced is feeling a lot of guilt, mostly related to my family and my 5-year-old son. I feel like I am not giving my all as even sports or lighthearted things suddenly have become challenging. Often depressed and dissatisfied, I feel as though I do not always reciprocate 100% the efforts of others who look after me.

Socially, my self-esteem and behavior around others has been impacted, because I fear my symptoms will show up or change randomly. The symptoms are always there, influencing my voice, sleep, diet, and general physical condition. Notwithstanding these difficulties, I try to keep as much normalcy as I can by balancing work and my personal life.

What has been the most challenging aspect of your Parkinson’s journey, and how have you managed to stay motivated throughout?

The psychological and emotional toll has been among the toughest aspects of my Parkinson’s path. Though the physical suffering was difficult, the heaviest toll on me was the feeling of frustration and helplessness. Dealing with YOPD already left the left side of my body weak, and after surgery to repair my ACL, my right side became significantly affected as well. This coupled with the understanding that healing could last up to nine months often made me feel as though I was losing the battle.

The unflinching support of my family, especially that of my wife and son— kept me going through these challenging times. My wife was my rock; she shared inspirational tales of those who had surmounted much more difficult obstacles in their life, therefore motivating me to keep on. Her support  allowed me to concentrate on recovery and bounce back with full confidence. Another inspiration came from my son, reminding me of the need to be present and resilient for him.

Apart from my family, my colleagues have  played a huge role in keeping me motivated. They encouraged me to keep pushing my boundaries, both physically and mentally, and gave me the confidence to face each day with a renewed sense of determination.

Though it has not been simple, with the emotional support of my loved ones and the strength I get from their belief in me, I have been motivated through one of the toughest obstacles of my life.

What does being a Microsoft MVP and contributing to the tech community mean to you?

Being a Microsoft MVP is about more than just title or recognition; it is about a dedication to significantly contribute to the tech community in meaningful ways. Every contribution — from blogging to event speaking to helping others solve their tech challenges, each contribution is an opportunity to make a positive impact. For me, it is about sharing knowledge, inspiring others, and being part of a larger community that thrives on innovation and collaboration.

Particularly on the tough days when other aspects of my life—such as dealing with health challenges—have been overwhelming, the MVP program has been a major source of encouragement. Knowing that my efforts are valued and that I can make a difference in someone’s learning journey motivates me to keep pushing forward. It gives me a sense of purpose and direction, even when times are difficult.

The community’s outpouring of support when I shared  my personal story at a monthly Nordics & Benelux MVP call was quite inspiring.

My peers’ encouragement and solidarity reminded me of the power of a strong, supporting group and helped drive me. It is this sense of belonging, coupled with the opportunity to give back, that makes being a Microsoft MVP so special to me.

Based on your personal experiences and insights, what advice would you offer to someone going through a personal, health, or professional crisis?

Life’s challenges, be they physical injuries or health conditions, test our limits. But they also reveal our strength, resilience, and capacity to adapt. Through my journey of recovery and community contribution, I have learned that while the path may be tough, the destination is worth every struggle. There will be more challenges, more hurdles to overcome, but with every step, you must grow stronger

Keep pushing the boundaries. Stay strong, stay motivated, and continue making a difference. We are all in this together, and together, we can achieve greatness. There is no limit to what we can achieve and what we can accomplish when we refuse to give up.

To everyone out there facing their own battles, remember this: Keep pushing. Your spirit is stronger than you think. Use your challenges as a springboard to greater heights. Together, we can achieve incredible things, one step at a time.

​Microsoft Tech Community – Latest Blogs –Read More