Industry-Relevant GenAI Course

The outcome from this week is to understand foundational machine learning principles which enable Generative AI to perform tasks like creating new content, such as text and images, by learning from extensive datasets. Topics Covered:

• Mathematical Foundations of Generative AI
• Understanding Machine Learning with respect to 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. Topics Covered: 

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

Topics Covered:

• LLMs and the genesis of Prompting
• 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
• Introduction to Responsible AI

Product Review Sentiment Analysis: Extract structured data( review date, product/service details, rating, summary, actionable items) from unstructured data using prompt engineering on OpenAI chat playground to get actionable insights for the business owners and generate first response for the users to reduce the Turn around Time.

The outcome from this week is to get up to speed on the Python concepts that are needed to automate prompt engineering at scale.
Topics Covered:

This week focuses on applying Generative AI to multi-modal tasks, enhancing workflows that require a combination of text, audio, and visual data.

Topics Covered: 

• Speech recognition using Generative AI
• Whisper Architecture
• Audio Transcription and Translation
• Dual Image-Text Understanding
• The CLIP Model from OpenAI
• Image Generation from Text Prompts
• Image Generation Architectures (GANs, Stable Diffusion)

The outcome from this week is to get proficient in using Promptflow to streamline prompt engineering tasks, optimize workflows-all without coding.

Topics Covered:

• Introduction to Prompt Flow
• Key Features of Prompt Flow
• Setting up and Integrating Prompt Flow with Azure AI Studio
• Navigating the Prompt Flow Interface
• Exploring Basic Workflows in Prompt Flow
• Generate completions for prompts and manage model parameters

This week focuses on applying Generative AI to multi-modal tasks, enhancing workflows that require a combination of text, audio, and visual data.

Topics Covered: 

• Speech recognition using Generative AI
• Whisper Architecture
• Audio Transcription and Translation
• Dual Image-Text Understanding
• The CLIP Model from OpenAI
• Image Generation from Text Prompts
• Image Generation Architectures (GANs, Stable Diffusion)

The outcome from this week is to get proficient in using Promptflow to streamline prompt engineering tasks, optimize workflows-all without coding.
Topics Covered:

• Getting setup with your Azure Open AI key and Python SDK (for coding)
• Getting setup with your Azure Open AI key and Prompt Flow (for no-code)
• Completions and Chat API
• Kinds of APIs, Models, Token, Rate Limits and Pricing
• Evaluating Generative AI Outputs
• 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. Topics Covered:

• Text-to-Label Generation (Classification)
• Framing text classification tasks as Generative AI problem
• Sentiment classification
• Assigning themes to a body of text 
• Aspect-based sentiment analysis

This week, you will learn how to apply Generative AI for content generation tasks across various business problem spaces. You will understand the complete workflow, from preparing data and designing effective prompts to evaluating results and deploying prototypes. Topics Covered:

• Content generation using Generative AI 
• Text-to-Text Generation (Summarization)
• Abstractive Summarization
• Evaluation Metrics (GPT Similarity)
• Privacy Protection in Generative AI
• Bias Mitigation
• Managing Generative AI Risks
• Security Risks (Prompt Injection, Insecure Outputs, Excessive Agency)

This week, you will learn how to build and evaluate Retrieval-Augmented Generation (RAG) systems, applying advanced Generative AI techniques for data retrieval and synthesis. You will explore the workflow of integrating Azure OpenAI with Azure AI Search and deploying a working solution on Azure or a local environment for a business use case. Topics Covered:

• Overview of Retrieval-Augmented Generation (RAG)
• Information Retrieval and Synthesis Workflow using Azure OpenAI
• Embedding Models and Vector Databases
• Evaluating RAG Workflow Outputs
• Deployment of RAG Flows
• Use Azure OpenAI API to generate responses based on your own data
• Incorporating User Feedback for Responsible AI

Aspect-based Classification for Sentiment Analysis: 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, comprehending foundational principles of ML, and becoming acquainted with prevalent machine learning methodologies. Topics Covered:

• 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.. Topics Covered:

• 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. Topics Covered:

• Identify features and uses for keyphrase 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.

Topics Covered:

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

• Understanding the concept of AI agents and their role 
• Exploring real-world applications of AI agents in various industries

• Overview of different agentic frameworks used in development 
• Key principles and methodologies for building efficient AI agents 
• Comparative overview of popular frameworks and their use cases

• Hands-on demonstration of AI-driven solutions using these platforms like OpenAI
• Discussion on challenges, best practices, and future possibilities

Join 3-hour expert-led masterclass and uncover how the MCP is transforming the future of app development and system intelligence

• Understand how apps talk to servers and where MCP fits in
• Explore how MCP simplifies complex logic using Al
• Live demo: Build your first MCP server from scratch
• Discover real-world use cases from different domains
• Ask your questions and get insights from the experts