Multiple updates

Removed first-person references (where possible), added code snippets, added notes about lab pre-requisites and how to get into a state to run labs individually.

Author: codingbandit

00_Introduction/README.md

@@ -18,4 +18,4 @@ The future of software involves combining AI and data services, also known as in
 
 ## Introduction
 
-This guide will walk you through the creating intelligent solutions that combines vCore-based Azure Cosmos DB for MongoDB vector search and document retrieval with Azure OpenAI services to build a chat bot experience. The guide includes labs that build and deploy a sample chat app using these technologies, with a focus on vCore-based Azure Cosmos DB for MongoDB, Vector Search, and Azure OpenAI using the Python programming language. For those new to using Azure OpenAI and Vector Search technologies, the guide includes explanations of the core concepts and techniques used when implementing these technologies.
+This guide will walks through the creating intelligent solutions that combines vCore-based Azure Cosmos DB for MongoDB vector search and document retrieval with Azure OpenAI services to build a chat bot experience. The guide includes labs that build and deploy a sample chat app using these technologies, with a focus on vCore-based Azure Cosmos DB for MongoDB, Vector Search, and Azure OpenAI using the Python programming language. For those new to using Azure OpenAI and Vector Search technologies, the guide includes explanations of the core concepts and techniques used when implementing these technologies.

01_Azure_Overview/README.md

@@ -99,7 +99,7 @@ Launch the Cloud Shell in a browser at [https://shell.azure.com](https://shell.a
 
 #### PowerShell Module
 
-The Azure portal and Windows PowerShell can be used for managing Azure Cosmos DB for Mongo DB API. To get started with Azure PowerShell, install the [Azure PowerShell cmdlets](https://learn.microsoft.com/en-us/powershell/module/az.cosmosdb/) for Cosmos DB with the following PowerShell command in an administrator-level PowerShell window:
+The Azure portal and Windows PowerShell can be used for managing Azure Cosmos DB for Mongo DB API. To get started with Azure PowerShell, install the [Azure PowerShell cmdlets](https://learn.microsoft.com/powershell/module/az.cosmosdb/) for Cosmos DB with the following PowerShell command in an administrator-level PowerShell window:
 
 ```PowerShell
 Install-Module -Name Az.CosmosDB

02_Overview_Cosmos_DB/README.md

@@ -1,27 +1,27 @@
 # Overview of Azure Cosmos DB
 
-[Azure Cosmos DB](https://learn.microsoft.com/en-us/azure/cosmos-db/introduction) is a globally distributed, multi-model database service that enables you to query and store data using NoSQL models using one of five APIs: SQL (document database), Cassandra (column-family), MongoDB (document database), Azure Table, and Gremlin (graph database). It provides turnkey global distribution, elastic scaling of throughput and storage worldwide, single-digit millisecond latencies at the 99th percentile, and guaranteed high availability with multi-homing capabilities. Azure Cosmos DB provides comprehensive service level agreements (SLAs) for throughput, latency, availability, and consistency guarantees, something not found in any other database service.
+[Azure Cosmos DB](https://learn.microsoft.com/azure/cosmos-db/introduction) is a globally distributed, multi-model database service that enables querying and storing data using NoSQL models using one of five APIs: SQL (document database), Cassandra (column-family), MongoDB (document database), Azure Table, and Gremlin (graph database). It provides turnkey global distribution, elastic scaling of throughput and storage worldwide, single-digit millisecond latencies at the 99th percentile, and guaranteed high availability with multi-homing capabilities. Azure Cosmos DB provides comprehensive service level agreements (SLAs) for throughput, latency, availability, and consistency guarantees, something not found in any other database service.
 
 ## Azure Cosmos DB and AI
 
 The surge of AI-powered applications has led to the need to integrate data from multiple data stores, introducing another layer of complexity as each data store tends to have its own workflow and operational performance. Azure Cosmos DB simplifies this process by providing a unified platform for all data types, including AI data. Azure Cosmos DB supports relational, document, vector, key-value, graph, and table data models, making it an ideal platform for AI applications. The wide array of data model support combined with guaranteed high availability, high throughput, low latency, and tunable consistency are huge advantages when building these types of applications.
 
 ## Azure Cosmos DB for Mongo DB
 
-The focus for this developer guide is [Azure Cosmos DB for MongoDB](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/introduction). Developers can leverage their current MongoDB expertise and use their preferred MongoDB drivers, SDKs, and tools simply by directing applications to the connection string for on the Azure Cosmos DB for MongoDB account.
+The focus for this developer guide is [Azure Cosmos DB for MongoDB](https://learn.microsoft.com/azure/cosmos-db/mongodb/introduction). Developers can leverage their current MongoDB expertise and use their preferred MongoDB drivers, SDKs, and tools simply by directing applications to the connection string for on the Azure Cosmos DB for MongoDB account.
 
 ### Azure Cosmos DB for Mongo DB API Architectures
 
-The [RU architecture](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/ru/introduction) for Azure Cosmos DB for MongoDB offers instantaneous scalability with zero warmup period, automatic and transparent sharding, and 99.999% availability. It supports active-active databases across multiple regions, cost-efficient, granular, unlimited scalability, real-time analytics, and serverless deployments where you pay only per operation.
+The [RU architecture](https://learn.microsoft.com/azure/cosmos-db/mongodb/ru/introduction) for Azure Cosmos DB for MongoDB offers instantaneous scalability with zero warmup period, automatic and transparent sharding, and 99.999% availability. It supports active-active databases across multiple regions, cost-efficient, granular, unlimited scalability, real-time analytics, and serverless deployments paying only per operation.
 
-[vCore-based Azure Cosmos DB for MongoDB architecture](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/introduction) integrates AI-based applications with your data, with text indexing for easy querying. Simplify your development process with high-capacity vertical scaling and free 35-day backups with a point-in-time restore (PITR).
+[vCore-based Azure Cosmos DB for MongoDB architecture](https://learn.microsoft.com/azure/cosmos-db/mongodb/vcore/introduction) integrates AI-based applications with private organizational data, with text indexing for easy querying. Simplify the development process with high-capacity vertical scaling and free 35-day backups with a point-in-time restore (PITR).
 
-The [choice between vCore and Request Units (RU)](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/choose-model) in Azure Cosmos DB for MongoDB API depends on the workload. A list of [compatibility and feature support between RU and vCore](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/compatibility) is available.
+The [choice between vCore and Request Units (RU)](https://learn.microsoft.com/azure/cosmos-db/mongodb/choose-model) in Azure Cosmos DB for MongoDB API depends on the workload. A list of [compatibility and feature support between RU and vCore](https://learn.microsoft.com/azure/cosmos-db/mongodb/vcore/compatibility) is available.
 
 vCore provides predictable performance and cost and is ideal for running high-performance, mission-critical workloads with low latency and high throughput. With vCore, the number of vCPUs and the memory the database needs is configurable and can be scaled up or down as needed.
 
 Conversely, RU is a consumption-based model that charges based on the number of operations the database performs, including reads, writes, and queries. RU is ideal for scenarios where the workload has unpredictable traffic patterns or a need to optimize cost for bursty workloads.
 
-A steady-state workload with predictable traffic patterns is best suited for vCore since it provides more predictable performance and cost. However, RU may be a better choice if the workload has unpredictable traffic patterns or requires bursty performance since it allows you to pay only for the resources used.
+A steady-state workload with predictable traffic patterns is best suited for vCore since it provides more predictable performance and cost. However, RU may be a better choice if the workload has unpredictable traffic patterns or requires bursty performance since it allows for paying only for the resources used.
 
->**NOTE**: AI-supporting workloads, such as vector search, must use vCore, as vector search is not supported with RU accounts.
+>**NOTE**: AI-supporting workloads, such as vector search, must use the vCore architecture, as vector search is not supported with RU accounts.

03_Overview_Azure_OpenAI/README.md

@@ -22,16 +22,16 @@ With Azure OpenAI, customers get the security capabilities of Microsoft Azure wh
 
 ## Azure OpenAI Data Privacy and Security
 
-Azure OpenAI stores and processes data to provide the service and to monitor for uses that violate the applicable product terms. Azure OpenAI is fully controlled by Microsoft. Microsoft hosts the OpenAI models in Microsoft Azure for your usage of Azure OpenAI, and does not interact with any services operated by OpenAI.
+Azure OpenAI stores and processes data to provide the service and to monitor for uses that violate the applicable product terms. Azure OpenAI is fully controlled by Microsoft. Microsoft hosts the OpenAI models in Microsoft Azure for the usage of Azure OpenAI, and does not interact with any services operated by OpenAI.
 
-Here are a few important things to know in regards to the security and privacy of your prompts (inputs) and completions (outputs), your embeddings, and your training data when using Azure OpenAI:
+Here are a few important things to know in regards to the security and privacy of prompts (inputs) and completions (outputs), embeddings, and training data when using Azure OpenAI:
 
 - are NOT available to other customers.
 - are NOT available to OpenAI.
 - are NOT used to improve OpenAI models.
 - are NOT used to improve any Microsoft or 3rd party products or services.
-- are NOT used for automatically improving Azure OpenAI models for your use in your resource (The models are stateless, unless you explicitly fine-tune models with your training data).
-- Your fine-tuned Azure OpenAI models are available exclusively for your use.
+- are NOT used for automatically improving Azure OpenAI models for use in the deployed resource (The models are stateless, unless explicitly fine-tuning models with explicitly provided training data).
+- Fine-tuned Azure OpenAI models are available exclusively for the account in which it was created.
 
 ## Azure AI Platform
 
@@ -52,57 +52,57 @@ Here's a list of the AI services within the [Azure AI platform](https://learn.mi
 
 | Service | Description |
 | --- | --- |
-| Azure AI Search | Bring AI-powered cloud search to your mobile and web apps |
+| Azure AI Search | Bring AI-powered cloud search to mobile and web apps |
 | Azure OpenAI | Perform a wide variety of natural language tasks |
 | Bot Service | Create bots and connect them across channels |
 | Content Safety | An AI service that detects unwanted contents |
-| Custom Vision | Customize image recognition to fit your business |
+| Custom Vision | Customize image recognition to fit the business |
 | Document Intelligence | Turn documents into usable data at a fraction of the time and cost |
 | Face | Detect and identify people and emotions in images |
 | Immersive Reader | Help users read and comprehend text |
 | Language | Build apps with industry-leading natural language understanding capabilities |
 | Machine Learning | ML professionals, data scientists, and engineers can use Azure Machine Learning in their day-to-day workflows to train and deploy models, such as those built from an open-source platform, such as PyTorch, TensorFlow, or scikit-learn |
 | Speech | Speech to text, text to speech, translation and speaker recognition |
 | Translator | Translate more than 100 languages and dialects |
-| Video Indexer | Extract actionable insights from your videos |
+| Video Indexer | Extract actionable insights from videos |
 | Vision | Analyze content in images and videos |
 
 > **Note:** Follow this link for additional tips to help in determining the which Azure AI service is most appropriate for a specific project requirement: <https://azure.microsoft.com/products/category/ai>
 
-The tools that you will use to customize and configure models are different from those that you'll use to call the Azure AI services. Out of the box, most Azure AI services allow you to send data and receive insights without any customization.
+The tools that used to customize and configure models are different from those used to call the Azure AI services. Out of the box, most Azure AI services allow for sending data and receive insights without any customization.
 
 For example:
 
-- You can send an image to the Azure AI Vision service to detect words and phrases or count the number of people in the frame
-- You can send an audio file to the Speech service and get transcriptions and translate the speech to text at the same time
+- Sending an image to the Azure AI Vision service to detect words and phrases or count the number of people in the frame
+- Sending an audio file to the Speech service and get transcriptions and translate the speech to text at the same time
 
 Azure offers a wide range of tools that are designed for different types of users, many of which can be used with Azure AI services. Designer-driven tools are the easiest to use, and are quick to set up and automate, but might have limitations when it comes to customization. The REST APIs and client libraries provide users with more control and flexibility, but require more effort, time, and expertise to build a solution. When using REST APIs and client libraries, there is an expectation that the developer is comfortable working with modern programming languages like C#, Java, Python, JavaScript, or another popular programming language.
 
 ### Azure Machine Learning
 
 [Azure Machine Learning](https://learn.microsoft.com/azure/machine-learning/overview-what-is-azure-machine-learning?view=azureml-api-2) is a cloud service for accelerating and managing the machine learning (ML) project lifecycle. ML professionals, data scientists, and engineers can use it in their day-to-day workflows to train and deploy models and manage machine learning operations (MLOps).
 
-Azure Machine Learning can be used to create a model or use a model built from an open-source platform, such as PyTorch, TensorFlow, or scikit-learn. Additionally, MLOps tools help you monitor, retrain, and redeploy models.
+Azure Machine Learning can be used to create a model or use a model built from an open-source platform, such as PyTorch, TensorFlow, or scikit-learn. Additionally, MLOps tools help monitor, retrain, and redeploy models.
 
-ML projects often require a team with a varied skill set to build and maintain. Azure Machine Learning has tools that help enable you to:
+ML projects often require a team with a varied skill set to build and maintain. Azure Machine Learning has tools that help enable:
 
-- Collaborate with your team via shared notebooks, compute resources, serverless compute, data, and environments
+- Collaboration within a team via shared notebooks, compute resources, serverless compute, data, and environments
 
-- Develop models for fairness and explainability, tracking and auditability to fulfill lineage and audit compliance requirements
+- Developing models for fairness and explainability, tracking and auditability to fulfill lineage and audit compliance requirements
 
-- Deploy ML models quickly and easily at scale, and manage and govern them efficiently with MLOps
+- Deploying ML models quickly and easily at scale, and manage and govern them efficiently with MLOps
 
-- Run machine learning workloads anywhere with built-in governance, security, and compliance
+- Running machine learning workloads anywhere with built-in governance, security, and compliance
 
-Enterprises working in the Microsoft Azure cloud can use familiar security and role-based access control for infrastructure. You can set up a project to deny access to protected data and select operations.
+Enterprises working in the Microsoft Azure cloud can use familiar security and role-based access control for infrastructure. A project can be set up to deny access to protected data and select operations.
 
 #### Azure Machine Learning vs Azure Open AI
 
 Many of the Azure AI services are suited to a very specific AI / ML need. The Azure Machine Learning and Azure OpenAI services offer more flexible usage based on the solution requirements.
 
 Here are a couple differentiators to help determine which of these to services to use when comparing the two:
 
-- Azure Machine Learning service is appropriate for solutions where a custom model needs to be trained specifically on your own data.
+- Azure Machine Learning service is appropriate for solutions where a custom model needs to be trained specifically on private data.
 
 - Azure OpenAI service is appropriate for solutions that require pre-trained models that provide natural language processing or vision services, such as the GPT-4 or DALL-E models from OpenAI.
 
@@ -124,7 +124,7 @@ Azure AI Studio enables teams to collaborate efficiently and effectively on AI p
 
 ![Azure AI Studio screenshot](images/2024-01-23-17-52-46.png)
 
-Tasks you can accomplish with Azure AI Studio include:
+Tasks accomplished using Azure AI Studio include:
 
 - Deploying models from the model catalog to real-time inferencing endpoints for client applications to consume.
 - Deploying and testing generative AI models in an Azure OpenAI service.