Generative AI Course

The outcome of this week is to understand foundational Machine Learning principles that enable Generative AI to perform tasks like creating new content, such as text and images, by learning from extensive datasets.

• Mathematical Foundations of Generative AI
• Understanding Machine Learning for Generative AI
• Connect NLP fundamentals with advanced Generative AI applications

The outcome of this week is to understand the Generative AI Landscape, fundamentals, and possibilities for businesses to solve problems and create products.

• 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)

The outcome of this week is to gain practical knowledge of Prompt Engineering and the ability to do it without code for various business use cases.

• LLMs and the genesis of Prompting
• How does the Attention Mechanism work? 
• A brief history of the GPT model series
• Accessing GPT through 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)
• Understand the concept of prompt engineering and its role in optimizing Azure OpenAI models' performance.
• Learn the capabilities of DALL-E in the Azure openAI service and Use the DALL-E playground in Azure OpenAI Studio

Problem Statement: Amazon needs an automated system that can efficiently analyze product reviews, extract critical information, and determine the sentiments expressed by customers. The solution should help the company gain insights into product performance and customer satisfaction.

This week's goal is to swiftly deepen grasp and expertise in the basics of Python. Concentrating on these fundamental elements, we strive to establish a robust foundation for tasks related to Python.

• Variables
• Data types
• Data Structures 
• Conditions and Loops
• Functions
• Strings
• Use natural language prompts to write code

The outcome from this week is to get up to speed on the Python concepts that are needed to automate prompt engineering at scale and understand the cost implications of using APIs.

• Store text in Python
• Edit, add, and delete text in Python
• How to read files in Python
• How to work with a database
• Manipulate string columns

The outcome of this week is to learn how to use the Azure Open AI API key and the Python SDK to leverage Generative AI at scale for solving business problems

• Getting set 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
• Generate completions to prompts and begin to manage model parameters
• Include clear instructions, request output composition, and use contextual content to improve the quality of the model's responses

The outcome of this week is to learn how to use Prompt Engineering to solve classification-type problems

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

The outcome of this week is to learn how to use Generative AI for content generation tasks across various business problem spaces

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

The outcome of this week is to learn how to setup an information retrieval and synthesis workflow on Azure or a local environment for a business use-case

• Overview of advanced application of Generative AI 
• Understand information retrieval and synthesis workflow using Azure Open AI
• Effectively communicate the core concepts of Retrieval-Augmented Generation (RAG) with the help of the LangChain package
• Use Azure OpenAI API to generate responses based on your own data

Problem Statement: The objective of this problem statement 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.

Identify characteristics of standard machine learning workloads, comprehend foundational principles of ML, and become acquainted with prevalent machine learning methodologies

• Identify regression, classification, and clustering machine learning scenarios
• Identify features and labels in a dataset for machine learning
• Describe the capabilities of Automated machine learning
• Describe data and compute services for data science and machine learning
• Describe model management and deployment capabilities in Azure Machine Learning

Recognize various computer vision solution types and discover Azure tools for handling computer vision tasks.

• Identify common types of computer vision solution
• Identify features of optical character recognition solutions
• Capabilities of the Azure AI Vision service
• Capabilities of the Azure AI Face detection service

Identify features of typical NLP workload scenarios and explore Azure tools and services applicable to NLP workloads.

• Identify features and uses for key phrase extraction
• Identify features and uses for entity recognition
• Identify features and uses for language modeling
• Identify features of common NLP Workload Scenarios
• Identify Azure tools and services for NLP workloads

Focus on recognizing features of generative AI solutions and understanding the capabilities offered by the Azure OpenAI Service.

• Identify features of generative AI solutions
• Identify capabilities of Azure OpenAI Service