Generative AI for Business with Microsoft Azure Open AI

This week you’ll understand the Generative AI Landscape, fundamentals and possibilities for businesses to solve problems and create products. Here's an overview of what will be covered this week:

• Defining AI, ML, DL, LLM and Generative Models
• Learning supervised and unsupervised ML tasks
• Understanding Generative and Discriminative AI
• A brief timeline of Generative AI
• A Peek into generative models
• Deconstructing the behavior of a large language models
• ML, DL and GenAI applications in business
• Hands-on Demonstration of popular tools (ChatGPT, DALL-E, BLIP)

This week you’ll understand the Cognitive services & the OpenAI service available on Microsoft Azure and learn how to use them. Here's an overview of what will be covered this week:

• Navigating the Azure Portal and Services
• Navigating the Azure Cognitive Services 
• Applying for and accessing the Azure Open AI service
• Creating an Azure Open AI service resource
• Getting setup with the Azure Open AI Studio
• Understanding the Azure Open AI model portfolio and pricing
• Walkthrough of key Azure Open AI services and an approach to build workflows

This week you’ll gain practical knowledge of Prompt Engineering and the ability to do it without code for various business use cases. Here's an overview of what will be covered this week:

• LLMs and the genesis of Prompting
• How does the Attention mechanism work? 
• A brief history of the GPT model series
• Accessing GPT through Microsoft Azure
• Designing prompts for business use cases using playground templates
• Prompting techniques (Prompt templates, precise instructions, chain of thought prompting)
• Ideating for prompts (prompt generation by induction, prompt paraphrasing)

Product Feedback Review & Sentiment Analysis
Create a prompt template that performs sentiment analysis on product reviews. The model should extract relevant information, such as product names, reviewer names, review ratings, review descriptions, and sentiment (positive or negative), to assist the company in understanding customer feedback better

This week, you’ll get up to speed on the Python concepts needed to automate prompt engineering at scale and understand the cost implications of using APIs. Here's an overview of what will be covered this week:

• Python Refresher (focused on prompt engineering)
• APIs and how they work
• Understanding cost - models, pricing, APIs, rate limits, etc

This week, you’ll learn how to use the Microsoft Azure Open AI API key and the Python SDK to leverage generative AI at scale for solving business problems. Here's an overview of what will be covered this week:

• Getting setup with your Azure Open AI key and Python SDK
• Completions and Chat API
• Kinds of APIs, Models, Token, Rate Limits and Pricing
• Evaluating Generative AI Outputs

This week you’ll learn how to use Prompt Engineering to solve classification-type problems. Here's an overview of what will be covered this week: 

• Framing text classification tasks as Generative AI problem
• Sentiment classification
• Assigning themes to a body of text 
• Aspect-based sentiment analysis

This week you’ll learn how to use Generative AI for content generation tasks across various business problem spaces. Here's an overview of what will be covered this week:

• Content generation using Generative AI 
• Abstractive summarization
• Text generation 
• Image generation

This week you’ll learn how to setup an information retrieval and synthesis workflow on Microsoft Azure or a local environment for a business use case.  Here's an overview of what will be covered this week:

• Overview of advanced application of Generative AI 
• Critical components of architecting conversational agents
• Demonstration of a conversational agent implementation

Aspect-based Text Classification for Sentiment Analysis

The objective is to use aspect-based classification for sentiment analysis to identify the aspects of a product or service that customers are most satisfied with and those that need improvement. This will help businesses understand their customers better and make data-driven decisions to improve their products or services. By improving customer satisfaction and loyalty, businesses can increase customer retention rates, reduce churn rates, and ultimately increase revenue.