Usama Wahab Khan

High-Level Summary: Framework for AI Adoption in Kingdom of Saudi Arabia (KSA) by SDAIA

noreply@blogger.com (Usama Wahab Khan) — Thu, 2 Oct 2025 10:58:00 -0700

Overview of the Framework

The document, issued by SDAIA (Saudi Data and AI Authority), outlines a comprehensive national strategy for adopting Artificial Intelligence (AI) across Saudi institutions. It aligns with Vision 2030 and aims to position Saudi Arabia as a global leader in AI.

Strategic Alignment

The framework is deeply integrated with Saudi Vision 2030, emphasizing digital transformation, economic diversification, and innovation.
SDAIA is designated as the national authority for AI and data governance, per Cabinet Resolution No. 292.

Goals of the Framework

Accelerate AI adoption in government and private sectors.
Build a sustainable and integrated institutional and technical environment.
Support initiatives aligned with national priorities.
Enhance governance and human capital development.

📌 Purpose & Vision

The framework is designed to accelerate smart transformation across public and private sectors.
It aligns with Vision 2030 and the National Strategy for Data & AI (NSDAI).
SDAIA serves as the national authority for AI governance, ethics, and implementation.

🧭 Strategic Objectives

Enable effective, safe, and sustainable AI adoption.
Provide a roadmap for planning, executing, and evaluating AI initiatives.
Support institutional performance, service quality, and resource sustainability.

🏛️ Framework Structure: Three Pillars

Directions
- Vision, goals, governance, initiative quality, and compliance.
Enablers
- Human capabilities, technical infrastructure, data quality, and AI models.
Outcomes
- Improved institutional performance, cost reduction, and enhanced service delivery.

🛠️ Implementation Guidelines

Short-Term (1–2 years): Automate operations, improve efficiency.
Mid-Term (3–5 years): Expand AI use, develop skills, support innovation.
Long-Term (>5 years): Deploy autonomous systems and advanced technologies.

🧠 AI Technologies Covered

Machine Learning (Supervised, Unsupervised, Reinforcement)
Deep Learning
Natural Language Processing (NLP)
Computer Vision
Generative AI
Smart Robotics

📊 Performance & Governance

Establish internal AI offices and supervisory units.
Align with SDAIA’s ethical and legal standards (e.g., PDPL).
Monitor adoption rates, model accuracy, and ROI.

👥 Human Capital Development

Build AI teams and promote continuous learning.
Collaborate with universities and training programs.
Encourage diversity and professional growth.

🗂️ Data Infrastructure

Ensure high-quality, secure, and accessible data.
Use automated validation and role-based access control.
Comply with national data protection laws.

🧩 Applications & Impact

Use AI for personalization, fraud detection, predictive analytics.
Improve employee productivity and service quality.
Achieve operational savings and strategic transformation.

Key AI Initiatives in Saudi Arabia

Saudi Arabia is rapidly positioning itself as a global AI leader through Vision 2030. Major initiatives include:

1. Project Transcendence

A $100 billion AI initiative aimed at building a robust AI ecosystem.
Focuses on data centers, startups, talent development, and partnerships with global tech firms like Google. [Saudi Arab...lobal tech]

2. National Strategy for Data & AI (NSDAI)

Led by SDAIA, the strategy aims to make Saudi Arabia a global hub for AI by 2030.
Goals include:
- Ranking among the top 15 countries in AI.
- Training over 20,000 AI specialists.
- Attracting ~75 billion SAR in AI investments. [National S...aia.gov.sa]

3. NEOM Smart City

NEOM integrates AI into urban infrastructure, energy, and mobility.
Hosts AI research centers and pilot programs with companies like Oracle and Kia. [Saudi Arab...I Strategy]

4. Healthcare AI Initiatives

AI is being used to address rising chronic diseases and cancer rates.
Programs like Seha Virtual Hospital and Mawid use AI for diagnostics and remote care. [Saudi Arab...are Demand]

5. AI in Transportation

Electric bus systems and smart mobility solutions are being deployed in cities like Riyadh and NEOM. [Saudi Arab...ies, 2030F]

6. Generative AI Development

Institutions like KAUST and KACST are pioneering generative AI research.
Focus on Arabic-language models and creative applicatio

Source

https://sdaia.gov.sa/en/SDAIA/about/Files/AIAdoptionFramework.pdf

Revolutionizing AI Development: Model Context Protocol (MCP) Unveiled in Azure AI Foundry

noreply@blogger.com (Usama Wahab Khan) — Sun, 20 Apr 2025 08:41:00 -0700

Reimagining AI Integration: The Power of MCP

Imagine a universal connector for AI applications—just like USB-C simplifies hardware connections, the Model Context Protocol (MCP) is revolutionizing how large language models (LLMs) interact with tools, data, and applications. MCP is an open protocol that simplifies the process of delivering context to LLMs, empowering developers to build powerful, intelligent agent-based solutions.

The concept of MCP originated from the challenges developers faced when building context-aware AI agents on top of large language models (LLMs) like GPT, Claude, or Gemini. These LLMs are stateless by design, meaning they don’t retain memory between interactions unless you provide that memory explicitly.

To solve this, Microsoft and the Azure SDK team introduced the Model Context Protocol, a vendor-agnostic open standard designed to manage and structure the context AI models receive from external tools and data sources.

Why Choose MCP?

MCP is purpose-built for developing intelligent agents and orchestrating complex workflows on top of LLMs. These AI models often need to interface with external data and services, and MCP provides a standardized way to make that integration seamless. Key benefits include:

🔌 Plug-and-play integrations: An expanding library of pre-built connections that LLMs can access out of the box.
Cross-platform compatibility: Avoids being locked into a single AI provider by offering flexible backend switching.
Secure by design: Encourages implementation of best practices for data protection within enterprise environments.

⚙️ Core Components of MCP

MCP changes how models manage and retrieve context, boosting their accuracy and conversational coherence. It introduces a structured framework with the following core elements:

Context Repository – Centralized storage for past interactions, queries, and AI outputs.
Dynamic Context Injection – Inserts relevant context during runtime to improve AI understanding.
Protocol Standardization – Ensures all contextual data is processed uniformly and accurately.
Adaptive Query Processing – Learns from previous interactions to tailor responses more precisely.

At its core, MCP follows a client-server architecture:
MCP Hosts: Applications like Claude Desktop or IDEs that want to access data through MCP.
MCP Clients: Protocol clients maintaining 1:1 connections with servers.
MCP Servers: Lightweight programs exposing specific capabilities through the standardized

MCP vs. Traditional RAG

While Retrieval-Augmented Generation (RAG) helps LLMs pull in external information, MCP elevates the approach by wrapping it in a smart context-management layer. Here's how it expands upon RAG:

Structures retrieved data into meaningful, persistent context
Introduces consistent communication protocols across sessions
Minimizes hallucinations with better historical awareness
Enables more nuanced and accurate responses through contextual refinement

In short, MCP transforms RAG into a system that not only retrieves information but sustains relevance across ongoing conversations.

MCP in Action: Azure MCP Server

Microsoft’s Azure MCP Server, now in public preview, brings MCP to life. It acts as a smart interface between AI agents and Azure’s cloud services, making it easier to:

Query data in Azure Cosmos DB
Read/write files in Azure Storage
Analyze system logs using Azure Monitor (KQL)
Manage settings through Azure App Configuration
Execute commands using Azure CLI

With just one command, developers can spin up the Azure MCP Server:

bash
npx -y @azure/mcp@latest server start

This sets up a robust backend that’s ready to handle interactions from any MCP-compliant AI agent.

MCP in Action: Azure AI Foundry

Enhanced Capabilities with MCP.

MCP in Action: Azure AI Foundry

Microsoft's Azure AI Foundry showcases how MCP isn't just theory—it’s a practical, production-ready approach to enhancing AI experiences across enterprise applications. Azure AI Foundry leverages the Model Context Protocol to bring together large language models, business data, and cloud services into a cohesive, intelligent system.

By embedding MCP into Azure AI Foundry, Microsoft enables organizations to:

Supercharge AI Search
Enterprise users can conduct intelligent searches across internal knowledge bases, file systems, and documentation. Thanks to MCP, these searches remain context-aware—tracking the history of what was asked before and refining answers accordingly.
Build Smarter Virtual Assistants
MCP makes it possible for virtual agents to maintain ongoing memory of a conversation. Whether it's a support bot, a sales assistant, or an internal service desk, Azure AI Foundry uses MCP to keep interactions fluid, relevant, and consistent across sessions.
Improve Decision-Making
By integrating with internal databases and telemetry sources, Foundry-enabled agents can analyze real-time operational data and provide actionable insights—helping decision-makers move from raw data to smart recommendations faster.
Seamless Knowledge Base Integration
With MCP’s structured approach, AI agents can easily connect to CRMs, wikis, ticketing systems, and document repositories—dynamically injecting relevant content during user interactions.
Enhance RAG Workflows
Azure AI Foundry builds on traditional Retrieval-Augmented Generation (RAG) models, using MCP to structure and persist retrieved data, ensuring conversations maintain continuity and context over time.

Collaborating with Semantic Kernel

For developers, MCP aligns perfectly with Semantic Kernel, Microsoft's open-source framework for integrating AI models with various data sources., developers can unlock the full potential of MCP for their projects.

Working with Semantic Kernel

MCP is fully compatible with Semantic Kernel, Microsoft’s open-source SDK designed to integrate AI models with real-world data sources. This synergy enables developers to build more intelligent, context-aware applications quickly.

Developers can use Semantic Kernel to extend MCP’s capabilities even further—whether for enterprise chatbots, intelligent assistants, or workflow automation solutions.

Example: Integrating MCP Server with Semantic Kernel

Scenario: Query Processing Workflow

A developer wants to create an AI agent that interacts with multiple data sources, such as a local database, APIs, and file systems, using Semantic Kernel and the MCP Server.

Steps:

MCP Configuration Begin by setting up the MCP Server to act as an intermediary for context handling. The server manages connections to various data sources.

json

{
    "MCPServer": {
        "DataSources": [
            { "Type": "LocalDatabase", "Connection": "db_connection_string" },
            { "Type": "API", "Endpoint": "https://api.example.com" },
            { "Type": "FileSystem", "Path": "/user/files" }
        ]
    }
}

Semantic Kernel Code

Using Semantic Kernel, you create a skill that interacts with the MCP Server:

python

from semantic_kernel import SemanticKernel
from mcp_client import MCPClient

# Initialize MCP Client
mcp_client = MCPClient(server_url="http://localhost:8000")

# Initialize Semantic Kernel
kernel = SemanticKernel()

# Define Skill for MCP Query
def query_mcp_skill(context):
    query = context["query"]
    response = mcp_client.query(query)
    return response

# Register Skill
kernel.register_skill("QueryMCP", query_mcp_skill)

# Execute Skill
input_context = {"query": "Retrieve latest sales data"}
result = kernel.execute_skill("QueryMCP", input_context)

print(result)  # Outputs data retrieved via MCP Server

Dynamic Context Injection Semantic Kernel can dynamically inject context into the query based on user interaction history:

python

def dynamic_context_skill(context):
    user_history = context["history"]
    context["query"] = f"{context['query']} based on {user_history}"
    return context["query"]

kernel.register_skill("DynamicContext", dynamic_context_skill)

Using Semantic Kernel with MCP Tools Microsoft provides detailed guides for using Semantic Kernel with MCP tools to streamline workflows. This allows developers to:

Fetch relevant context from MCP Server.
Enable dynamic skill chaining for complex workflows.
Maintain context-awareness across interactions.

Ref: https://devblogs.microsoft.com/azure-sdk/introducing-the-azure-mcp-server/

DeepSeek R1: Revolutionizing AI on Azure AI Foundry and GitHub

noreply@blogger.com (Usama Wahab Khan) — Wed, 29 Jan 2025 20:00:00 -0800

DeepSeek R1 is now available on Azure AI Foundry and GitHub, marking a significant milestone in AI development. This advanced reasoning model offers powerful capabilities with minimal infrastructure investment, making cutting-edge AI more accessible to developers and enterprises.

Key Features of DeepSeek R1

Advanced Reasoning

DeepSeek R1 excels in complex reasoning tasks, making it ideal for applications requiring sophisticated problem-solving abilities.

Scalability

Built on the trusted and scalable Azure AI Foundry, DeepSeek R1 seamlessly integrates into enterprise workflows and cloud-based solutions.

Cost-Efficiency

With minimal infrastructure investment, DeepSeek R1 democratizes access to AI capabilities, making it feasible for startups and large enterprises alike.

Security and Compliance

DeepSeek R1 has undergone rigorous red teaming and safety evaluations, ensuring adherence to responsible AI principles and industry security standards.

Quick Tutorials: Getting Started with DeepSeek R1

1 Deploy DeepSeek R1 on Azure AI Foundry

Step 1: Sign in to Azure AI Foundry and navigate to the Model Catalog.

Step 2: Search for DeepSeek R1 and select the desired model variant.

Step 3: Click Deploy, configure resources (CPU/GPU), and integrate with your application via Azure OpenAI API.

Use the Azure SDK for Python to interact with the model:

import openai

client = openai.AzureOpenAI(

api_key="YOUR_AZURE_API_KEY",

endpoint="YOUR_AZURE_ENDPOINT"

)

response = client.Completions.create(

model="deepseek-r1",

prompt="What is the future of AI?",

max_tokens=100

)

print(response.choices[0].text)

Resources & Further Reading

Azure AI Foundry: DeepSeek R1

DeepSeek R1 GitHub Repository

Azure AI Model Deployment Guide

DeepSeek R1 brings the power of advanced reasoning AI to businesses and developers, enabling more intelligent, efficient, and scalable applications. Ready to explore?

Free Exam, Get Ready to Fast-Track Your Career with the Microsoft Certified: Fabric Analytics Engineer Associate Certification!

noreply@blogger.com (Usama Wahab Khan) — Tue, 5 Nov 2024 23:08:00 -0800

For a limited time, the Microsoft Fabric Community team is offering 5,000 free DP-600 exam vouchers to eligible Fabric Community members.

We'll be sharing more information in this article throughout the month of November. Subscribe to stay up to date!

:loudspeaker: November 5th Update

You can request your free voucher starting on November 19th at 9:00 AM PT (Seattle, USA timezone). The URL to request your free voucher will be https://aka.ms/iamready/dp600.

Eligibility Criteria:

To be eligible for this limited-time offer, you must:

Join the Fabric Community if you haven't already.
Not already be a Microsoft Certified: Fabric Analytics Engineer Associate (DP-600).
Register for and complete all modules in the Microsoft Learn Challenge | Ignite Edition: Fabric Challenge.
- Pre-registration is open now!
- On November 19th at 8:00 AM PT, you will be able to see the collection of Learn modules you must complete.
Do not submit your request form before completing the challenge or your request will be denied.
Be confident that you can take and pass exam DP-600 by December 31, 2024.
Agree to these terms and conditions.

Already registered for the challenge? Start preparing for the exam and complete a few of the required modules now.

Steps to Prepare:

Watch the Get Certified! Fabric Analytics Engineer (DP-600) on-demand series.
Complete these Fabric learning modules.
Start studying for the exam.

About Microsoft Fabric:

Microsoft Fabric is an end-to-end analytics and data platform designed for enterprises that require a unified solution. It encompasses data movement, processing, ingestion, transformation, real-time event routing, and report building. Fabric integrates components like Data Engineering, Data Factory, Data Science, Real-Time Analytics, Data Warehouse, and Databases into a cohesive stack. It simplifies data integration, governance, and security across clouds and analytics engines, helping data teams collaborate, discover, and act on data with AI tools.

About the DP-600 Exam:

The DP-600 exam, titled "Implementing Analytics Solutions Using Microsoft Fabric," assesses a candidate's ability to plan, implement, and manage data analytics solutions. The exam lasts 100 minutes and includes 40-60 multiple-choice and multiple-response questions. To pass, candidates must score at least 700 out of 1000. The exam covers topics such as data modeling, data transformation, Git-based source control, SQL, DAX, and PySpark.

Free Consultation and Mentorship

Are you preparing for the DP-600 exam and looking for guidance? Reach out to Usama Wahab Khan, a Microsoft MVP and experienced technology executive, for free consultation and mentorship. Connect with him on LinkedIn or follow him on X.

Generative AI: A Primer for Users and Business Leaders

noreply@blogger.com (Usama Wahab Khan) — Wed, 23 Oct 2024 09:07:00 -0700

In the rapidly evolving world of technology, Generative Artificial Intelligence (AI) has emerged as a groundbreaking force, transforming how we create, innovate, and conduct business. This article aims to demystify Generative AI for both the novice user and the seasoned business leader, providing a detailed example to illustrate its potential.

### What is Generative AI?

Generative AI refers to the subset of artificial intelligence focused on creating new content, whether that be text, images, or even code. It leverages complex algorithms and neural networks to analyze vast amounts of data, learning patterns and styles to generate original outputs. This technology powers a range of applications, from chatbots and virtual assistants to advanced design systems.

### For the Basic User

If you're new to Generative AI, think of it as a highly advanced assistant that can help you with a variety of tasks. For instance, if you're writing an email or a report, Generative AI can suggest complete sentences or paragraphs that sound as if you wrote them yourself. It can also create realistic images or music based on your descriptions or help you code by providing snippets that fit your project's needs.

### For the Business Leader

For business leaders, Generative AI is a game-changer. It can significantly reduce the time and cost associated with content creation and product development. In marketing, for example, AI can generate personalized content that resonates with different segments of your audience, increasing engagement and conversion rates. In product design, it can rapidly prototype new ideas, speeding up the innovation cycle and bringing products to market faster.

### A Detailed Example

Imagine a retail company looking to design a new line of clothing. Traditionally, this process would involve designers sketching ideas, creating prototypes, and going through several iterations before finalizing a design. With Generative AI, the company can input current fashion trends, desired styles, and materials into an AI system, which then generates a range of design options. These options can be refined and altered until the perfect design is achieved, all within a fraction of the time it would normally take.

### Conclusion

Generative AI is not just a futuristic concept; it's a present-day tool that offers immense benefits for individuals and businesses alike. By automating and enhancing creative processes, it allows for greater efficiency, innovation, and personalization. As this technology continues to advance, it will undoubtedly open up new horizons for human creativity and enterprise.

Whether you're a basic user curious about AI's capabilities or a business leader seeking to leverage AI for competitive advantage, the journey into Generative AI is well worth embarking on. It promises to be a key driver of progress in the digital age, reshaping our approach to creation and problem-solving.

Retirement of Real-Time Streaming in Power BI: What You Need to Know and How to Migrate

noreply@blogger.com (Usama Wahab Khan) — Wed, 16 Oct 2024 18:30:00 -0700

What’s Changing?

Microsoft is making key changes to the real-time streaming capabilities in Power BI. If you’re currently using real-time semantic models for your streaming data insights, it’s essential to plan for the upcoming changes:

• Starting October 31, 2024: Creation of new real-time semantic models will no longer be supported. This includes Push semantic models, Streaming semantic models, PubNub streaming, and Streaming data tiles.

• By October 31, 2027: Existing real-time semantic models will be fully retired and no longer supported.

These dates are critical for organizations relying on Power BI for real-time insights. Microsoft has committed to working with existing customers on migration strategies leading up to the 2027 deadline, with potential for date adjustments as necessary.

What’s the Alternative?

Microsoft recommends transitioning to Real-Time Intelligence (RTI) solutions available in Microsoft Fabric, which provides a more comprehensive platform for real-time insights and data streaming. Fabric’s capabilities go beyond what Power BI’s real-time streaming offered, delivering robust solutions for event-driven scenarios, data logs, and streaming data.

For new real-time streaming requirements, leveraging Microsoft Fabric is the best way forward. It enables data streaming from multiple sources, geospatial analysis, and actionable insights all within a unified platform.

Key Features of Microsoft Fabric Real-Time Intelligence

1. Centralized Real-Time Hub: Fabric’s Real-Time Hub serves as the central repository for streaming data, allowing easy access, exploration, and sharing across your organization. It integrates with sources like Azure Event Hubs, Azure IoT Hub, and more, ensuring seamless data flow.

2. Event Streams: With a no-code interface, you can capture and transform real-time events from various sources, including Azure, AWS, and Google, and route them to the desired destinations.

3. Event Processing: Fabric allows for real-time data cleansing, filtering, transformation, and aggregation. You can also create derived streams for more tailored data sharing and processing.

4. Eventhouses: These specialized engines are designed for time-based event analytics, enabling quick and powerful querying of both structured and unstructured data.

5. Visualization & Insights: Seamlessly integrate with Power BI to visualize your data and create dashboards and reports for real-time insights. Alerts can trigger actions based on changing data patterns, turning insights into actions.

6. Data Activator: Fabric’s Data Activator lets you respond to real-time data by triggering alerts and actions, such as sending notifications or invoking workflows when certain data conditions are met.

Moving forward to Migrate Your Existing Real-Time Models

If you’re using real-time semantic models in Power BI, the transition to Microsoft Fabric should be part of your future planning. Key steps in the migration process include:

• Review Current Models: Evaluate the existing real-time semantic models in use and assess their role in your business workflows.

• Explore Fabric’s Capabilities: Understand how Fabric’s Real-Time Hub, Event Streams, and Eventhouses can replace or enhance your current real-time streaming setup.

• Plan Your Migration: Begin planning to transition before the 2027 deadline. Work closely with Microsoft or a certified partner to ensure a smooth migration.

For further guidance, visit aka.ms/RTIblog, which will be continually updated with migration resources and best practices.

Final Thoughts

While the retirement of real-time streaming in Power BI marks the end of an era, it also opens the door to more powerful and flexible real-time intelligence solutions in Microsoft Fabric. By preparing now and exploring the possibilities in Fabric, you can continue to harness the power of real-time data to drive smart, timely decisions across your organization.

Don’t wait until the last minute—start your migration planning today to ensure you stay ahead of these changes!

Introducing Meta LLaMA 3: A Leap Forward in Large Language Models

noreply@blogger.com (Usama Wahab Khan) — Thu, 18 Apr 2024 17:40:00 -0700

Meta has recently unveiled its latest innovation in the realm of artificial intelligence: the LLaMA 3 large language model. This state-of-the-art model represents a significant advancement in AI technology, offering unprecedented capabilities and accessibility.

What is LLaMA 3?

LLaMA 3 is the third iteration of Meta's large language model series. It is an open-source model that has been fine-tuned with instructions to optimize its performance across a wide array of tasks. The model comes in two sizes: one with 8 billion parameters and another with a colossal 70 billion parameters.

Features and Capabilities

The LLaMA 3 models are designed to excel in language understanding and generation, making them highly effective for applications such as dialogue systems, content creation, and complex problem-solving. Some of the key features include:

-Enhanced Reasoning

LLaMA 3 demonstrates improved reasoning abilities, allowing it to handle multi-step problems with ease.

-Multilingual and Multimodal Future

Plans are underway to make LLaMA 3 multilingual and multimodal, further expanding its versatility.

Extended Context Windows

The new models support longer context windows, enabling them to maintain coherence over larger text spans.

The Meta Llama 3 models have been enhanced with a substantial increase in training tokens, reaching 15trillion, which greatly improves their ability to grasp the nuances of language. The context window has been expanded to 8,000 tokens, effectively doubling the previous model's capacity and allowing for the processing of more extensive text excerpts, which aids in making more informed decisions. Additionally, these models employ a novel Tiktoken-based tokenizer that boasts a128,000-token vocabulary, resulting in a more efficient encoding of characters per token. Meta has observed improved performance in both English and multilingual benchmark assessments, confirming the models' strong capabilities in handling multiple languages.

Unmatched Performance Excellence

The introduction of our 8B and 70B parameter LLaMA 3 models marks a significant advancement beyond the capabilities of LLaMA 2, setting a new benchmark for large language models (LLMs) at these scales. Enhanced pretraining and refined post-training techniques have elevated our models to the pinnacle of performance, making them the premier choice in the current landscape for 8B and 70B parameter models. Notable enhancements in our post-training processes have led to a considerable decrease in incorrect rejections, bolstered model alignment, and enriched the variety of responses generated by the models. Furthermore, we've observed a remarkable enhancement in functions such as logical reasoning, code creation, and adherence to instructions, rendering LLaMA 3 more adaptable and responsive to user guidance.

Accessibility and Community Support

In line with Meta's commitment to open innovation, LLaMA 3 is made available to the broader community. It can be accessed on various platforms, including AWS, Databricks, Google Cloud, and Microsoft Azure, among others¹. This move is intended to foster a wave of AI innovation across different sectors.

It's now available on Azure

https://techcommunity.microsoft.com/t5/ai-machine-learning-blog/introducing-meta-llama-3-models-on-azure-ai-model-catalog/ba-p/4117144

Trust and Safety

Meta has introduced new trust and safety tools, such as LLaMA Guard 2 and Code Shield, to ensure the responsible use of LLaMA 3. These tools are part of a comprehensive approach to address the ethical considerations associated with deploying large language models¹.

The Impact of LLaMA 3

The release of LLaMA 3 is poised to have a profound impact on the AI landscape. By providing a powerful tool that is openly accessible, Meta is enabling developers and researchers to push the boundaries of what's possible with AI. The model's capabilities in understanding and generating human-like text will unlock new possibilities in various fields, from education to customer service.

As we look to the future, LLaMA 3 stands as a testament to Meta's dedication to advancing AI technology while maintaining a focus on ethical and responsible development. It's an exciting time for AI, and LLaMA 3 is at the forefront of this technological revolution.

More details

(1) Introducing Meta Llama 3: The most capable openly available LLM to date. https://ai.meta.com/blog/meta-llama-3/.

(2) Meta Llama 3. https://llama.meta.com/llama3/.

#Meta #llama #Azure #MVPBuzz #generativeai #GenAI #LLM #Opensource

Power BI With Copilot

noreply@blogger.com (Usama Wahab Khan) — Sun, 14 Apr 2024 10:05:00 -0700

Get Started with Copilot for Power BI and Create Reports Faster

Ready to unlock the power of AI in your data analysis? Copilot, a new feature in Power BI Desktop, is here to help you create reports faster and easier. With Copilot's assistance, you can generate report summaries, suggest report content, and even create entire report pages based on your high-level instructions.

What You'll Need to Use Copilot:

Access: You'll need write access to a workspace assigned to a paid Power BI capacity (P1 or higher) or a paid Fabric capacity (F64 or higher) with Copilot enabled by your administrator.
Power BI Desktop: Ensure you're using the latest version of Power BI Desktop.

Getting Started with Copilot:

Enable Copilot (Admin): Your administrator needs to enable Copilot in Microsoft Fabric and activate the tenant switch.
Open the Copilot Pane: Click the Copilot icon in the ribbon to open the Copilot pane.
Welcome and Workspace Selection: The first time you use Copilot, a dialog will appear prompting you to choose a compatible workspace. Select any workspace assigned to the required capacity.
Start Your Interaction: Once you've selected a workspace, you'll see a welcome card. Click "Get started" to begin using Copilot.

How Copilot Can Help You:

Summarize Your Data Model: Gain a clearer understanding of your data with Copilot's summaries of your Power BI semantic model. This can help you identify key insights and streamline your report building process.

Suggest Report Content: Stuck on what to include in your report? Copilot can analyze your data and propose relevant topics for you to explore. From the Copilot pane, select "Suggest content for a report" to get started.
Create Report Pages: Save time crafting reports from scratch. Provide Copilot with a high-level prompt related to your data, and Copilot will generate a customizable report page with relevant tables, fields, measures, and charts. Here are some examples of prompts you can use:

Analyze performance across different shifts based on metrics like good count, reject count, and alarm count.
Evaluate production line efficiency and overall equipment effectiveness.
Compare costs, materials, and their impact on production for each product

Important Considerations:

The Copilot button will always be visible in the ribbon, but functionality requires signing in, admin enabled tenant settings, and workspace access as mentioned earlier.
The workspace you select for Copilot usage doesn't have to be the same one where you publish your report.
Copilot is currently in preview, and its responses are generated using AI, so always double-check your work for accuracy.
There are limitations for creating report pages with certain connection modes like live connect to SSAS and AAS, and real-time streaming in Power BI.

Here are four examples of what Copilot can generate.

Stay Updated:

Keep an eye out for the latest Copilot enhancements by following the monthly Power BI feature summary blogs.

https://learn.microsoft.com/en-us/power-bi/create-reports/copilot-introduction

Embrace AI-powered report creation with Copilot and transform your data analysis workflow!

RAG live Demo with Azure and openAi

noreply@blogger.com (Usama Wahab Khan) — Sat, 6 Apr 2024 18:12:00 -0700

How to use Azure Cosmos DB for Mongo DB v-Core as vector database for RAG (retrieval augmented generation ) Part 2

noreply@blogger.com (Usama Wahab Khan) — Sun, 3 Mar 2024 22:39:00 -0800

Unlocking the Power of Retrieval Augmented Generation (RAG) with Azure and Cosmos DB: A Comprehensive Guide (part1)

Introduction

Retrieval augmented generation (RAG) is a pattern that combines a large-scale language model (LM) with a retrieval system to generate natural language responses that are relevant, diverse, and informative. The retrieval system can be a vector database that stores pre-computed embeddings of documents or passages that can be efficiently searched by similarity. In a previous article, we discussed the importance of RAG patterns in modern LM-based generative AI applications and highlighted some of the options that are available in Azure that can be used for RAG components. In this article, we will focus on one of the options, Azure Cosmos DB for Mongo DB vCore, and show how it can be used as a vector database for RAG. We will also provide some sample code to demonstrate how to store and query embeddings using Azure Cosmos DB for Mongo DB vCore.

What is a Vector Database?

A vector database is specifically designed to store and manage vector embeddings—mathematical representations of data in a high-dimensional space. In this space, each dimension corresponds to a feature of the data, and tens of thousands of dimensions might be used to represent sophisticated data. These vector embeddings are used in various domains, including similarity search, recommendation systems, natural language processing, and large language models (LLMs).

Here are some key points about vector databases:

Vector Search: Vector databases enable efficient similarity search based on vector representations.
Retrieval-Augmented Generation (RAG): An increasingly popular use case involves integrating vector databases with LLMs to generate contextually relevant and accurate responses to user prompts. This approach overcomes token limits and reduces the need for frequent fine-tuning of updated data.

What is vector search?

Vector search lets you find items that are similar in meaning or content, not just by matching a specific field. This can help you with tasks like searching for related text, finding images that match a theme, making suggestions, or spotting outliers. To do a vector search, you need to use a machine-learning model that turns your data into vectors (lists of numbers) that capture their features. This is done by using an embeddings API, such as Azure OpenAI Embeddings or Hugging Face on Azure. Then, you compare the distance between the vectors of your data and the vector of your query. The closer the vectors are, the more similar the data is to your query.

With native vector search support, you can make the most of your data in applications that use the OpenAI API. You can also build your own solutions that use vector embeddings.

Vector Database /Index option in Azure.

Service	Description
Azure Cosmos DB for Mongo DB vCore	Store your application data and vector embeddings together in a single MongoDB-compatible service featuring native support for vector search.
Azure Cosmos DB for PostgreSQL	Store your data and vectors together in a scalable PostgreSQL offering with native support for vector search.
Azure Cosmos DB for NoSQL with Azure AI Search	Augment your Azure Cosmos DB data with the semantic and vector search capabilities of Azure AI Search.

Azure Cosmos DB Vector Database Extension for MongoDB vCore:

Store your application data and vector embeddings together in a single MongoDB-compatible service.
Benefit from native support for vector search.
Avoid the extra cost of moving data to a separate vector database.
Achieve data consistency, scale, and performance.
Learn more ¹.

Azure Cosmos DB Vector Database Extension for PostgreSQL:

Store your data and vectors together in a scalable PostgreSQL offering.

Leverage native support for vector search.
Seamlessly integrate vector capabilities without additional database migration costs.
Learn more ¹.

Azure Cosmos DB for NoSQL with Azure AI Search:

Augment your Azure Cosmos DB data with semantic and vector search capabilities.
Combine the power of NoSQL with AI-driven search.
Enhance retrieval-augmented generation scenarios.
Learn more ².

Remember, vector databases play a crucial role in harnessing the potential of vector embeddings, enabling advanced search, and enhancing AI-driven applications. Choose the option that best aligns with your specific use case and requirements! 🚀

A simple and effective way to store, index, and search vector data in Azure Cosmos DB for MongoDB vCore is to use the built-in vector search feature. This feature allows you to perform similarity searches on arrays of numbers, such as embeddings, using MongoDB's $vector query operator. By using this feature, you can avoid the need to move your data to more expensive vector databases and integrate your AI-driven applications smoothly with your other data.

What is Azure Cosmos DB for Mongo DB v Core?

Azure Cosmos DB for Mongo DB vCore is a fully managed, scalable, and secure service that provides API compatibility with MongoDB. It allows you to use the familiar MongoDB tools and drivers to build applications that can leverage the benefits of Azure Cosmos DB, such as global distribution, automatic scaling, and SLA-backed availability and latency. Azure Cosmos DB for Mongo DB vCore also supports MongoDB's aggregation pipeline, which enables complex queries and transformations on the data. One of the features that makes Azure Cosmos DB for Mongo DB vCore suitable for RAG is the support for MongoDB's $vector query operator, which allows you to perform similarity searches on arrays of numbers, such as embeddings.

RAG Pipeline Process

Load Datastore
Clean data
Chunking Data
Convert Chunks into vector embedding with LLM
Create a Vector Index with Converted vector embeddings.

How to use Azure Cosmos DB for Mongo DB vCore as a vector database for RAG?

To use Azure Cosmos DB for Mongo DB vCore as a vector database for RAG, you need to follow these steps:

Create an Azure Cosmos DB account with API for MongoDB and select the vCore model. You can choose the number of regions, availability zones, and throughput depending on your needs. You can also enable features such as geo-fencing, encryption at rest, and role-based access control.
Create a database and a collection in your Azure Cosmos DB account. You can use any schema and indexing strategy that suits your application, but you need to ensure that the field that stores the embeddings is indexed as an array. You can use the MongoDB shell or any MongoDB driver to connect to your Azure Cosmos DB account and create the database and collection.
Populate your collection with documents that contain the embeddings and any other metadata that you need for your RAG application. You can use any method to generate the embeddings, such as a pre-trained model or a custom model. You can also use any format to store the embeddings, such as a list or a base64-encoded string. You can use the MongoDB shell or any MongoDB driver to insert the documents into your collection. Alternatively, you can use Azure Data Factory or Azure Databricks to import data from various sources into your collection.
Query your collection using the $vector operator to perform a similarity search on the embeddings. You can use the MongoDB shell or any MongoDB driver to query your collection. The $vector operator takes an array of numbers as input and returns the documents that have the most similar arrays in the specified field. You can also use other query operators and aggregation stages to filter, sort, and project the results. You can integrate the query results with your LM to generate natural language responses that are relevant, diverse, and informative.

Here is some sample code that shows how to use Azure Cosmos DB for Mongo DB vCore as a vector database for RAG:

Preliminaries

First, let's start by installing the packages that we'll need later.

! pip install numpy

! pip install openai 
! pip install pymongo
! pip install python-dotenv
! pip install azure-core
! pip install azure-cosmos
! pip install tenacity

First, let's start by installing the packages that we'll need later.

import json

import datetime
import time

from azure.core.exceptions import AzureError
from azure.core.credentials import AzureKeyCredential
import pymongo

import openai
from dotenv import load_dotenv
from tenacity import retry, wait_random_exponential, stop_after_attempt
from openai import AzureOpenAI

Please use the example.env as a template to provide the necessary keys and endpoints in your own .env file. Make sure to modify the env_name accordingly.

print(openai.VERSION) #should be greater than 1.0xx

loading endpoint and secret API keys

from dotenv import dotenv_values

# specify the name of the .env file name 
env_name = "example.env" # following example.env template change to your own .env file name
config = dotenv_values(env_name)

cosmosdb_endpoint = config['cosmos_db_api_endpoint']
cosmosdb_key = config['cosmos_db_api_key']
cosmosdb_connection_str = config['cosmos_db_connection_string']

COSMOS_MONGO_USER = config['cosmos_db_mongo_user']
COSMOS_MONGO_PWD = config['cosmos_db_mongo_pwd']
COSMOS_MONGO_SERVER = config['cosmos_db_mongo_server']

openai.api_type = config['openai_api_type']
openai.api_key = config['openai_api_key']
openai.api_base = config['openai_api_endpoint']
openai.api_version = config['openai_api_version']
embeddings_deployment = config['openai_embeddings_deployment']
completions_deployment = config['openai_completions_deployment']

Let's start by creating an Azure Cosmos DB for MongoDB vCore Resource following this quick start guide: https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/quickstart-portal

Then copy the connection details (server, user, pwd) into the config.json file.

Azure OpenAI

Create an Azure OpenAI resource following this quickstart: https://learn.microsoft.com/azure/ai-services/openai/how-to/create-resource?pivots=web-portal

Deploy a completions embeddings model

For more information on, go here: https://learn.microsoft.com/azure/ai-services/openai/how-to/completions

For more information on, go here: https://learn.microsoft.com/azure/ai-services/openai/how-to/embeddings

Copy the endpoint, key, and deployment names for (the embeddings model, and completions model) into the config.json file.

Finally, let's set up our Azure OpenAI resource Currently, access to this service is granted only by application. You can apply for access to Azure OpenAI by completing the form at https://aka.ms/oai/access. Once you have access, complete the following steps:

Load data and create embeddings

Here we load a sample dataset containing descriptions of Azure services, and then we use Azure OpenAI to create vector embeddings from this data.

# Load text-sample.json data file. Embeddings will need to be generated using the function below.
#data_file = open(file="../../DataSet/AzureServices/text-sample.json", mode="r")

# OR Load text-sample_w_embeddings.json which has embeddings pre-computed
data_file = open(file="./text-sample_w_embeddings.json", mode="r") 
data = json.load(data_file)
data_file.close()

# Take a peek at one data item

print(json.dumps(data[0], indent=2))

{ "id": "1", "title": "Azure App Service", "content": "Azure App Service is a fully managed platform for building, deploying, and scaling web apps. You can host web apps, mobile app backends, and RESTful APIs. It supports a variety of programming languages and frameworks, such as .NET, Java, Node.js, Python, and PHP. The service offers built-in auto-scaling and load balancing capabilities. It also provides integration with other Azure services, such as Azure DevOps, GitHub, and Bitbucket.", "category": "Web", "titleVector": [ -0.0017071267357096076, -0.01391641329973936, 0.0017036213539540768, -0.018410328775644302,

Creating Open AI Client for Library 1.xx

client = AzureOpenAI(
  api_key =config['openai_api_key'],  
  api_version = "2023-05-15",
  azure_endpoint =config['openai_api_endpoint']
)

@retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(10))
def generate_embeddings(text):
    '''
    Generate embeddings from string of text.
    This will be used to vectorize data and user input for interactions with Azure OpenAI.
    '''
   
   
    embeddings = client.embeddings.create(input = "test", model="myembeddingmodel").data[0].embedding 
    time.sleep(0.2) # rest period to avoid rate limiting on AOAI for free tier
    return embeddings

# Generate embeddings for title and content fields
n = 0
for item in data:
    n+=1
    title = item['title']
    content = item['content']
    title_embeddings = generate_embeddings(title)
    content_embeddings = generate_embeddings(content)
    item['titleVector'] = title_embeddings
    item['contentVector'] = content_embeddings
    item['@search.action'] = 'upload'
    print("Creating embeddings for item:", n, "/" ,len(data), end='\r')
# Save embeddings to sample_text_w_embeddings.json file
with open("./text-sample_w_embeddings.json", "w") as f:
    json.dump(data, f)

Connect and setup Cosmos DB for MongoDB vCore

Set up the connection

mongo_client = pymongo.MongoClient("mongodb+srv://user:pass@xxxx.mongocluster.cosmos.azure.com/
?tls=true&authMechanism=SCRAM-SHA-256&retrywrites=false&maxIdleTimeMS=120000")

Set up the DB and collection

# create a database called TutorialDB
db = mongo_client['ExampleDB']

# Create collection if it doesn't exist
COLLECTION_NAME = "ExampleCollection"

collection = db[COLLECTION_NAME]

if COLLECTION_NAME not in db.list_collection_names():
    # Creates a unsharded collection that uses the DBs shared throughput
    db.create_collection(COLLECTION_NAME)
    print("Created collection '{}'.\n".format(COLLECTION_NAME))
else:
    print("Using collection: '{}'.\n".format(COLLECTION_NAME))

## Use only if re-reunning code and want to reset db and collection
collection.drop_index("VectorSearchIndex")
mongo_client.drop_database("ExampleDB")

Create the vector index

IMPORTANT: You can only create one index per vector property. That is, you cannot create more than one index that points to the same vector property. If you want to change the index type (e.g., from IVF to HNSW) you must drop the index first before creating a new index.

IVF

IVF is the default vector indexing algorithm, which works on all cluster tiers. It's an approximate nearest neighbors (ANN) approach that uses clustering to speed up the search for similar vectors in a dataset.

db.command({
  'createIndexes': 'ExampleCollection',
  'indexes': [
    {
      'name': 'VectorSearchIndex',
      'key': {
        "contentVector": "cosmosSearch"
      },
      'cosmosSearchOptions': {
        'kind': 'vector-ivf',
        'numLists': 1,
        'similarity': 'COS',
        'dimensions': 1536
      }
    }
  ]
})

HNSW (preview ) you skip this

NSW is a graph-based data structure that organizes vectors into clusters and subclusters. It facilitates fast approximate nearest neighbor search, achieving higher speeds with improved accuracy. As a preview feature, you can enable HNSW using Azure Feature Enablement Control (AFEC) by selecting the “mongoHnswIndex” feature. For detailed instructions, refer to the enable preview features documentation.

Keep in mind that HNSW operates on M50 cluster tiers and higher while in preview. 🚀

Upload data to the collection

A simple insert_many() to insert our data in JSON format into the newly created DB and collection.

collection.insert_many(data)

Vector Search in Cosmos DB for MongoDB vCore

# Simple function to assist with vector search
def vector_search(query, num_results=3):
    query_embedding = generate_embeddings(query)
    embeddings_list = []
    pipeline = [
        {
            '$search': {
                "cosmosSearch": {
                    "vector": query_embedding,
                    "path": "contentVector",
                    "k": num_results #, "efsearch": 40 # optional for HNSW only 
                },
                "returnStoredSource": True }},
        {'$project': { 'similarityScore': { '$meta': 'searchScore' }, 'document' : '$$ROOT' } }
    ]
    results = collection.aggregate(pipeline)
    return results

Let's run a test query below.

query = "What are the services for running ML models?"
results = vector_search(query)
for result in results: 
#     print(result)
    print(f"Similarity Score: {result['similarityScore']}")  
    print(f"Title: {result['document']['title']}")  
    print(f"Content: {result['document']['content']}")  
    print(f"Category: {result['document']['category']}\n")  

Q&A over the data with GPT-3.5

Finally, we'll create a helper function to feed prompts into the Completions model. Then we'll create an interactive loop where you can pose questions to the model and receive information grounded in your data.

#This function helps to ground the model with prompts and system instructions.

def generate_completion(prompt):
    system_prompt = '''
    You are an intelligent assistant for Microsoft Azure services.
    You are designed to provide helpful answers to user questions about Azure services given the information about to be provided.
        - Only answer questions related to the information provided below, provide 3 clear suggestions in a list format.
        - Write two lines of whitespace between each answer in the list.
        - Only provide answers that have products that are part of Microsoft Azure.
        - If you're unsure of an answer, you can say ""I don't know"" or ""I'm not sure"" and recommend users search themselves."
    '''

    listmessages=[
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": user_input},
    ]

    for item in results:
        listmessages.append({"role": "system", "content": prompt['content']})
    
            
    response = client.chat.completions.create(
    model="demo35", # model = "deployment_name".
    messages=listmessages
)
    
    return response.choices[0].message.content

generate_completion("Where i can host container in azure")  # test Qustion with out RAG

# Create a loop of user input and model output. You can now perform Q&A over the sample data!

user_input = ""
print("*** Please ask your model questions about Azure services. Type 'end' to end the session.\n")
user_input = input("Prompt: ")
while user_input.lower() != "end":
    results_for_prompt = vector_search(user_input)
   # print(f"User Prompt: {user_input}")
    completions_results = generate_completion(results_for_prompt)
    print("\n")
    print(completions_results)
    user_input = input("Prompt: ")

Source Code GitHub https://github.com/Usamawahabkhan/Usamawahabkhan-RAG-Azure-CosmosDb-MongoDb-vcore/blob/main/AzureOpenAI-CosmosDB-MongoDB-vCore_Tutorial.ipynb

Conclusion

In this article, we have seen how to use RAG, a powerful technique for generating natural language answers from large-scale text corpora, with Cosmos DB for MongoDB vCore, a fully managed and scalable database service that supports the MongoDB API. We have shown how to upload data to the collection, create a vector index, and perform vector search using PyMongo and Faiss. We have also discussed some of the benefits and challenges of using RAG and Cosmos DB for MongoDB vCore for building GenAI applications based on LLMs.

RAG is becoming an essential part of any modern GenAI app based on LLMs, as it enables generating of high-quality and diverse responses from multiple sources of knowledge. Storing data is crucial and requires security, availability and scalability, where Azure Cosmos DB can be a very valuable choice. However, there are other options available in Azure and open source, depending on the application requirements and compliance. We encourage you to explore different alternatives and find the one that suits your needs best.

#MVPBuz #Azure #AzureCosmosDB #MongoDBvCore #VectorDatabase #RAG #NoSQL#AIApplications #DataManagement #LLM #GenerativeAI

Free Certification Exam Offer from Microsoft

noreply@blogger.com (Usama Wahab Khan) — Fri, 17 Jan 2020 02:35:00 -0800

Great news came from Microsoft. Anyone who will participate as registered attendance at Microsoft Ignite tour will get a chance to give one free Microsoft Exam. Microsoft Ignite the tour is also taking place in Dubai on February 10–11, 2020 where Cloud and Data lover can find tons of awesome sessions and learning content. if you're based in UAE don't miss the chance to participate in it. I hope so see you At Ignite. guys if you have any questions about feel free to catch on twitter Usama wahab Khan

Microsoft Ignite tour Website Link for Registrations

https://www.microsoft.com/en-us/ignite-the-tour/#cities

how to claim free exam Click here

Eligible exams for this offer

By Product Exams

Azure AI-100: Designing and Implementing an Azure AI Solution

AZ-103: Microsoft Azure Administrator

AZ-120: Planning and Administering Microsoft Azure for SAP Workloads

AZ-203: Developing Solutions for Microsoft Azure

AZ-300: Microsoft Azure Architect Technologies

AZ-301: Microsoft Azure Architect Design

AZ-400: Microsoft Azure DevOps Solutions

AZ-500: Microsoft Azure Security Technologies

AZ-900: Microsoft Azure Fundamentals

DP-100: Designing and Implementing a Data Science Solution on Azure

DP-200: Implementing an Azure Data Solution

DP-201: Designing an Azure Data Solution 

Dynamics 365 and Power Platform MB-200: Microsoft Power Platform + Dynamics 365 Core

MB-210: Microsoft Dynamics 365 Sales

MB-220: Microsoft Dynamics 365 Marketing

MB-230: Microsoft Dynamics 365 Customer Service

MB-240: Microsoft Dynamics 365 Field Service

MB-300: Microsoft Dynamics 365: Core Finance and Operations

MB-310: Microsoft Dynamics 365 Finance

MB-320: Microsoft Dynamics 365 Supply Chain Management, Manufacturing

MB-330: Microsoft Dynamics 365 Supply Chain Management

MB-400: Microsoft PowerApps + Dynamics 365 Developer

MB-500: Microsoft Dynamics 365: Finance and Operations Apps Developer

MB-600: Microsoft Power Platform + Dynamics 365 Solution Architect (exam will not be available in beta until after December 31, 2019)

MB-700: Microsoft Dynamics 365: Finance and Operations Apps Solution Architect (exam will not be available in beta until after December 31, 2019)

MB-900: Microsoft Dynamics 365 Fundamentals

PL-900: Microsoft Power Platform Fundamentals

Microsoft 365 MD-100: Windows 10

MD-101: Managing Modern Desktops

MS-100: Microsoft 365 Identity and Services

MS-101: Microsoft 365 Mobility and Security

MS-200: Planning and Configuring a Messaging Platform

MS-201: Implementing a Hybrid and Secure Messaging Platform

MS-300: Deploying Microsoft 365 Teamwork

MS-301: Deploying SharePoint Server Hybrid

MS-500: Microsoft 365 Security Administration

MS-600: Building Applications and Solutions with Microsoft 365 Core Services

MS-700: Managing Microsoft Teams

MS-900: Microsoft 365 Fundamentals

Terms and conditions

This exam offer is available to eligible individuals who have attended Microsoft Ignite 2019 or a Microsoft Ignite The Tour 2019-2020 event.

This exam offer may be redeemed to take one (1) Microsoft Certification exam, delivered at an authorized Pearson Vue testing center or through a Pearson Vue online proctoring site.

This exam offer is exam-specific and only redeemable for select Microsoft exams.

This exam offer is available for a period beginning the first day of the event at which you received this exam offer and expires 180 days after the last day of such event.

This exam offer entitles you to register for one (1) exam prior to the offer expiration date.

This exam offer expiration date cannot be extended under any circumstances.

This exam offer is not valid for, and may not be redeemed by, either: 1) government officials; or 2) residents of Hong Kong, Switzerland, Israel, Germany, Russia, or Sweden. *This offer is not available to government officials or residents as noted as it exceeds the limits permissible under Microsoft policy and local law.

This exam offer may only be redeemed once.

This exam offer may not be redeemed or exchanged for cash, credit or refund.

This exam offer is nontransferable and is void if you alter, revise or transfer it in any way.

This exam offer is only offered to individuals who have registered and attended an official Microsoft Ignite 2019 or Microsoft Ignite The Tour 2019-2020 event.

for more details

https://docs.microsoft.com/en-us/learn/certifications/microsoft-ignite-free-certification-exam-offer?WT.mc_id=msignitethetour2019_akawwlflag_-email-event

Thanks to Microsoft and MVP Team for another MVP Award kit

noreply@blogger.com (Usama Wahab Khan) — Sun, 9 Jul 2017 00:46:00 -0700

Present Big Data Three V and Microsoft Azure Stream Analytics Microsoft Cloud Roadshow

noreply@blogger.com (Usama Wahab Khan) — Sun, 21 May 2017 00:40:00 -0700

It was my pleasure to be part of data platform roadshow event! I Talked about Microsoft Azure Platform and Big Data and PowerBI with Microsoft Real Time data Stream solution for IOT and Real Time dashboards with Azure Stream Analytics Microsoft Cloud Roadshow. Event Arranged by Microsoft for Data Science Community in UAE.

GIS &a Remote Sensing Annual Scientist Forum by DUBAI MUNICIPALITY

noreply@blogger.com (Usama Wahab Khan) — Mon, 6 Mar 2017 01:06:00 -0800

Thanks to Government of Dubai and DUBAI MUNICIPALITYGIS) for great speaking opportunity @ GIS &a Remote Sensing Annual Scientist Forum by govt Dubai

Microsoft Certified Trainer (MCT) 2017

noreply@blogger.com (Usama Wahab Khan) — Tue, 14 Feb 2017 01:01:00 -0800

I am highly honored to be a part MCT community again this year. it's my 5th year in as Microsoft Certifed Trainer. I am proud of my this Achievement.

I am Offering Training for
SharePoint (Full Stack)
Office365 (Full Stack)
SQL Server
PowerBI (Full Stack)
MVC/ASP (Full Stack)
Visual Studio
PowerBI
Azure (Full Stack)

for Contact email me at ukhan@evoluitontech.ae

I am honored to Received 4th time Microsoft Most Valuable Professional Award - 2017

noreply@blogger.com (Usama Wahab Khan) — Sat, 14 Jan 2017 05:00:00 -0800

I am really glad to Share this News that i received Microsoft Most Valuable Professional award 2017 for Office and Service. its my 4th MVP award.I am really honored and humble to receive this award. Previously i received MVP Award for SharePoint and Office server and Services 2014,2015,2016. I am thankful to almighty ALLAH and friends and family for their great support. My Special thanks to my Global SP community ( UAE SharePoint Community , UAE Microsoft Dev Community ,SPS Dubai, SPS Paris, AOS Community , European SharePoint Community,SP brunei Usergroup,User Group,Power BI Global ) and Microsoft, MVP Program, Microsoft Gulf, SharePoint Team. i received MVP Award for My technical contribute last year(2016) with great support of local Microsoft and our Dx Team (Fatima Faturinwal,Ahmed ) and my friend Ovais Mahboob MVP and Co-Organizers local dev UG . I hope this year i will able to share more knowledge and do really valuable contributions.

MVP Award Program

For more than two decades, the Microsoft MVP Award is our way of saying "Thanks!" to outstanding community leaders. The contributions MVPs make to the community, ranging from speaking engagements, to social media posts, to writing books, to helping others in online communities, have incredible impact. Among other benefits, MVPs get early access to Microsoft products, direct communication channels with our product teams and are invited to the Global MVP Summit, an exclusive annual event hosted in our global HQ in Redmond. They also have a very close relationship with the local Microsoft teams in their area, who are there to support and empower MVPs to address needs and opportunities in the local ecosystem.

Find out what's new!

Contact Info
Usama Wahab Khan
CTO Evolution Technologies
twitter :@usamawahabkhan
linkedin : usamawahabkhan
Facebook : FB Page Usama Wahab Khan

Free Beta Exam for SharePoint Server 2016 70-339

noreply@blogger.com (Usama Wahab Khan) — Tue, 28 Jun 2016 15:27:00 -0700

Microsoft has made many fundamental changes in SharePoint 2016 Release. Its first ever SharePoint with Cloud first and Mobile First Approach. thats mean now SharePoint developer ,SharePoint IT Pro , SharePoint Consultants, SharePoint UX Designer, SharePoint Trainers need to update themself with new skills. Microsoft also Released new certifications for SharePoint 2016 one of the is Managing Microsoft SharePoint Server 2016 (Beta) AKA 70-339 Exam Link. So ASAP because its limited time offer. to be noted my all friends from Turkey, Pakistan, India, China, Vietnam can not avail this offer. 70-339 is IT-Pro Exam and It Focus on majorly SharePoint infrastructure and hybrid seniors. if any of participants need training or have any question about SharePoint Feel free to Contact me Twitter or Facebook.

Main topics for 70-339
Design SharePoint infrastructure (15–20%)
Plan authentication and security (15–20%)
Plan workload optimization (15–20%)
Plan productivity solutions (15–20%)
Manage search capabilities (10–15%)
Plan and configure cloud services (5–10%)
Monitor and optimize a SharePoint environment (10–15%)
Preparation options

More Details about Exam Registration and Free copen Click Here

Happy SharePointing
Usama Wahab Khan
MVP,MCT,MCC CTO EP Technologies

Speaking at Online Webinar on PowerBI Join us

noreply@blogger.com (Usama Wahab Khan) — Sun, 13 Mar 2016 11:03:00 -0700

Join us on 23rd March - Big Day

Join Usama Wahab Khan Yousfzai (MVP & MCT)
Register the Informative Power BI Session. ( everything you need to know about PowerBI)

Linke : http://1drv.ms/1TA433N
Session Mode: Skype for Business (Online)
Date : 23 march 2016
Time: 8pm to 9:30pm

I am Speaking At SharePoint Saturday Paris 2016

noreply@blogger.com (Usama Wahab Khan) — Fri, 11 Mar 2016 11:55:00 -0800

I am Proud to announce that I am Speaking at SPS Paris 2016. I`ve been really honoured to been selected by SPS Paris Team among the top International speakers. my session will focus on Microsoft PowerApps under the title of SharePoint Forms Evolution.

About SharePoint Saturday Paris 2016.

Administrateurs SharePoint, utilisateurs, architectes, développeurs, et autres professionnels travaillant sur les technologies et associées comme:

Nous vous donnons rendez-vous le 28 mai 2016 à la Tour Montparnasse, 33 Avenue du maine, 75015 Paris pour le SharePoint Saturday Paris 2016.

English

SharePoint administrators, end users, architects, developers, and other

professionals that work with Microsoft and associated technologies like:

Please join us on May 28th 2016 at the Montparnasse Tower located at 33 Avenue du maine, 75015 Paris, for the SharePoint Saturday Paris 2016.

Need complementary information about the event? You can reach us by sending an email to: spsparis@outlook.com

Introduction to Microsoft Power Apps at Microsoft Gulf

noreply@blogger.com (Usama Wahab Khan) — Sun, 28 Feb 2016 06:44:00 -0800

First ever Demo of Microsoft New Product for Rapid Mobile App creation for end user demonstrated in Dubai Microsoft Gulf.

Ovais Mehboob ‪#‎aspnercore‬ ‪#‎introduction‬ ‪#‎DubaidevUG‬ ‪#‎MVP‬

Microsoft PowerApps Introduction by Usama Wahab Khan MVP from Usama Wahab Khan SharePoint/office365/azure Architect

More Event Details :Here

Renewed Microsoft Certified Trainer (MCT)

noreply@blogger.com (Usama Wahab Khan) — Tue, 16 Feb 2016 02:17:00 -0800

By the grace of Almighty ALLAH. I become again Microsoft Certified Trainer (MCT) for 2016. I am MCT from since 2012. its been wonderful experenice to be a part of MCT Community. I hope so this year will able to focus on cloud training and share my knowledge by delivering quality training in this region.

Thank you

Usama Wahab Khan

MVP,MCT,MCC,PMPc

Introductory sessions on ASP.NET 5 MVC 6 and Microsoft Power BI By MVPs at Microsoft Dubai

noreply@blogger.com (Usama Wahab Khan) — Sun, 17 Jan 2016 04:16:00 -0800

Hello everyone!

Our next meet-up will take place on January 20th from 6.00 pm onwards. This gathering is the opportunity for programmers to meet & get to know each other. Please join us to share experiences and network with other developers as well as the Microsoft technical experts.

Agenda:

6:00-7:00: Introduction to ASP.NET 5 and MVC 6

Speaker: Ovais Mehboob, Microsoft MVP - Visual Studio and Development Technologies

This will be an introductory session that help developers to start developing web applications using ASP.NET 5 and MVC 6

7:00-8:00: Introduction to Microsoft Power BI

Speaker: Usama Wahab Khan, Microsoft MVP - Office Servers and Services

In this session you will see how easily you can start creating personalized dashboards and reports on your data using Microsoft Power BI

8:00 onwards: Pizza & Networking

@usamawahabkhan

Registration and Details
http://www.meetup.com/Microsoft-Developers-UAE-Meetup/events/227797631/?rv=me1&_af=event&_af_eid=227797631&https=off

I am Speaking at SharePoint Saturday Lahore: Join Us

noreply@blogger.com (Usama Wahab Khan) — Sun, 17 Jan 2016 03:54:00 -0800

we are coming to Lahore for Share Saturday Lahore (SPS Lahore) along with my MVP Friends Michael Noel and Joel on 23 of Jan 2016. Join us.

SharePoint Saturday Lahore

SharePoint administrators, end users, architects, developers, and other professionals that work with Microsoft SharePoint Technologies will meet for the 2nd SharePoint Saturday Lahore event on January 23, 2016.

Event Venue

Superior University Campus - Main Raiwind Road Lahore. Click here for location map.

Call for speakers and sponsors is NOW OPEN!

Sponsor Packet for SPS Lahore is located here .

Speakers :

Registration and Details : http://www.spsevents.org/city/Lahore/Lahore2016

I am Speaking at SharePoint Saturday Dubai : Join Us

noreply@blogger.com (Usama Wahab Khan) — Sun, 17 Jan 2016 03:47:00 -0800

About SharePoint Saturday Dubai

SharePoint administrators, end users, architects, developers, and other professionals that work with Microsoft SharePoint Technologies will meet for the 5th SharePoint Saturday Dubai event on January 30, 2016.

Event Venue

Microsoft Gulf Office, Building 8, Dubai Internet City, Dubai. Click he re for location map.

Speakers :

My Session Sessions

- SharePoint and Office 365 API with angularjs
Registration : https://www.eventbrite.com/e/sharepoint-saturday-dubai-2016-tickets-19699566935

I have been awarded by Microsoft again for Microsoft Most Valuable Professional Award 2016

noreply@blogger.com (Usama Wahab Khan) — Wed, 6 Jan 2016 00:43:00 -0800

#Hat-trick !Yes Thanks to Almighty ALLAH and my family for support. I Just Received an email from Microsoft "We are pleased to present you with the 2016 Microsoft® MVP Award! This award is given to exceptional technical community leaders who actively share their high quality, real world expertise with others. We appreciate your outstanding contributions in Office Servers and Services technical communities during the past year." Thank you everyone in Community Ovais Mehboob Sohail Hanif Baloch Madridista Salman Ahmad. Special thank to Hande Kayadeniz Torkan Fatima Furniturewala for Their Support. Thank you Microsoft for this honer. #Microsoft #MVPBuzz #MVPMEA

Usama Wahab Khan

High-Level Summary: Framework for AI Adoption in Kingdom of Saudi Arabia (KSA) by SDAIA

Overview of the Framework

Strategic Alignment

Goals of the Framework

📌 Purpose & Vision

🧭 Strategic Objectives

🏛️ Framework Structure: Three Pillars

🛠️ Implementation Guidelines

🧠 AI Technologies Covered

📊 Performance & Governance

👥 Human Capital Development

🗂️ Data Infrastructure

🧩 Applications & Impact

Key AI Initiatives in Saudi Arabia

1. Project Transcendence

2. National Strategy for Data & AI (NSDAI)

3. NEOM Smart City

4. Healthcare AI Initiatives

5. AI in Transportation

6. Generative AI Development

Revolutionizing AI Development: Model Context Protocol (MCP) Unveiled in Azure AI Foundry

Reimagining AI Integration: The Power of MCP

Why Choose MCP?

⚙️ Core Components of MCP

At its core, MCP follows a client-server architecture:​

MCP vs. Traditional RAG

MCP in Action: Azure MCP Server

MCP in Action: Azure AI Foundry

Enhanced Capabilities with MCP.

MCP in Action: Azure AI Foundry

Collaborating with Semantic Kernel

Working with Semantic Kernel

Example: Integrating MCP Server with Semantic Kernel

Scenario: Query Processing Workflow

Steps:

DeepSeek R1: Revolutionizing AI on Azure AI Foundry and GitHub

Free Exam, Get Ready to Fast-Track Your Career with the Microsoft Certified: Fabric Analytics Engineer Associate Certification!

:loudspeaker: November 5th Update

Eligibility Criteria:

Steps to Prepare:

About Microsoft Fabric:

About the DP-600 Exam:

Free Consultation and Mentorship

Generative AI: A Primer for Users and Business Leaders

Retirement of Real-Time Streaming in Power BI: What You Need to Know and How to Migrate

What’s Changing?

What’s the Alternative?

Introducing Meta LLaMA 3: A Leap Forward in Large Language Models

Power BI With Copilot

Get Started with Copilot for Power BI and Create Reports Faster

RAG live Demo with Azure and openAi

How to use Azure Cosmos DB for Mongo DB v-Core as vector database for RAG (retrieval augmented generation ) Part 2

Introduction

What is a Vector Database?

What is vector search?

Vector Database /Index option in Azure.

What is Azure Cosmos DB for Mongo DB v Core?

RAG Pipeline Process

How to use Azure Cosmos DB for Mongo DB vCore as a vector database for RAG?

Preliminaries

Azure OpenAI

Load data and create embeddings

Creating Open AI Client for Library 1.xx

Connect and setup Cosmos DB for MongoDB vCore

Set up the connection

Set up the DB and collection

Create the vector index

IVF

HNSW (preview ) you skip this

Upload data to the collection

Vector Search in Cosmos DB for MongoDB vCore

Q&A over the data with GPT-3.5

Conclusion

Free Certification Exam Offer from Microsoft

Eligible exams for this offer

Thanks to Microsoft and MVP Team for another MVP Award kit

Present Big Data Three V and Microsoft Azure Stream Analytics Microsoft Cloud Roadshow

GIS &a Remote Sensing Annual Scientist Forum by DUBAI MUNICIPALITY

Microsoft Certified Trainer (MCT) 2017

At its core, MCP follows a client-server architecture: