Generative AI Course

Generative AI for Business with Microsoft Azure OpenAI

Generative AI Training with Microsoft Azure OpenAI

Application Closes 30^th Apr 2024

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Application closes 30^th Apr 2024

Key Highlights of the Generative AI Program

Microsoft Azure AI Fundamentals training
Prompt Engineering without and with code
Azure Lab access with OpenAI Studio
Learn from experienced industry mentors
8+ hands-on case studies, 2 hands-on projects
Dedicated Program Manager and Academic Support

Skills you will learn

Prompt Engineering
Using OpenAI API
Using Python SDK for Prompt Engineering
Microsoft Azure Cloud Services for AI

Globally recognized education platform

4.8
4.7
4.7
4.97

Great Learning alumni work at top companies

Curriculum

This curriculum, divided into five modules, offers a comprehensive understanding of Azure OpenAI and Generative AI. The first module delves into AI, ML, LLMs, and Prompt Engineering, guiding you through Azure's OpenAI services. The second module offers proficiency in using the Azure OpenAI API key and Python SDK, allowing hands-on learning with Generative AI applications in text classification and summarization. At the end of the course, you'll have the skill set to apply Generative AI across various tasks, from content generation to creating prompts, all without writing a line of code

Unit 1

Module-1: Introduction to Generative AI

This module serves as an introduction to the rapidly evolving field of Generative Artificial Intelligence (AI). The primary focus for this week is to gain a comprehensive understanding of the Generative AI landscape, including its foundational concepts and the potential it holds for businesses in problem-solving and product creation.

Week-1: Generative AI: Business Landscape & Overview

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

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)

Unit 2

Module-2: AI900: Azure AI Fundamentals

This module is designed to provide a foundational understanding of machine learning, AI concepts, and associated Microsoft Azure services. While Azure AI Fundamentals can be beneficial in preparing for Azure role-based certifications such as Azure Data Scientist Associate or Azure AI Engineer Associate, it's important to note that it is not a mandatory prerequisite for any of these certifications.

Week-2: Artificial Intelligence workloads and fundamental principles of machine learning on Azure

Focus on recognizing features of typical AI workloads, understanding principles for responsible AI, and gaining familiarity with common machine learning techniques.

Identify features of common AI workloads
Identify guiding principles for responsible AI
Identify common machine learning techniques
Describe core machine learning concepts
Describe Azure Machine Learning capabilities

Week-3: Computer Vision and NLP workloads on Azure

Recognize various computer vision solution types and discover Azure tools for handling computer vision tasks. Additionally, Identify features of typical NLP workload scenarios and explore Azure tools and services applicable to NLP workloads.

Identify common types of computer vision solution
Identify Azure tools and services for computer vision tasks
Identify features of common NLP Workload Scenarios
Identify Azure tools and services for NLP workloads

Week-4: Generative AI workloads on Azure

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

Unit 3

Module-3: Leveraging Generative AI for Business Applications

The module revolves around three core pillars - understanding Generative AI, exploring Azure OpenAI services, and mastering Prompt Engineering. In this enriching journey, you will delve into foundational concepts of AI, Machine Learning (ML), Deep Learning (DL), Large Language Models (LLMs), and their applications across various industries. You will gain hands-on experience with cutting-edge generative tools and explore the vast capabilities of Azure OpenAI services. Lastly, you will learn the intricate art of Prompt Engineering, mastering the design and implementation of effective prompts without the need for coding.

Week-5: Prompt Engineering without Code

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.

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)

Week-6: Project: Product Feedback Review & Sentiment Analysis

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

Objective: The objective of this project is to 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.

Week-7: Learning Break

Unit 4

Module-4: Python for Generative AI

In this course, you’ll have a solid understanding of Python programming needed for Generative AI, and be equipped with the skills to start creating your own generative AI projects. Whether you’re a seasoned programmer looking to expand your AI knowledge or a complete beginner interested in the field, this module will set you up with the programming skills you need.

Week-8: Python Basics

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

Variables
Data types
Data Structures
Conditions and Loops
Functions
String Operations
- Concatenation
- String formatting
- String Indexing
- Slicing
- String length
- String methods
- String searching and Manipulation
- String Conversion

Week-9: Python for learning Prompt Engineering

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

Unit 5

Module-5: Designing Generative AI Solutions with Azure Open AI

This advanced module plunges deeper into the workings of LLMs, teaching you how to automate prompt engineering and other Generative AI applications at scale using Python. Learn to set up your Azure Open AI.

API key and import the Python library/SDK to work with various Generative AI models. Master the Completions API, ChatCompletions API, and Embeddings API, understanding their rates, limits, and pricing. The course then moves to practical applications of Generative AI in text classification and summarization, with hands-on exercises such as classifying medical records and assigning themes to finance news articles.

Week-10: Prompt Engineering at Scale

The outcome from 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 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

Week-11: Classification Tasks with Generative AI

The outcome from 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

Week-12: Content Generation and Summarization with Generative AI

The outcome from 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
Image generation

Week-13: Information Retrieval and Synthesis workflow with Gen AI

The outcome from 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
Critical components of architecting conversational agents
Demonstration of a conversational agent implementation

Week-14: Final Project: Aspect-based Classification for Sentiment Analysis

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.

Earn a Certificate from Microsoft Azure

Enhance your resume with a certificate in Generative AI for Business with Microsoft Azure OpenAI from Great Learning and Microsoft Azure and share it with your professional network

* Image for illustration only. Certificate subject to change.

Industry-relevant syllabus

Learn Top In-Demand Tools

Gain hands-on experience with cutting-edge tools and explore the vast capabilities of Generative AI

Azure AI Services
Python
Azure OpenAI Service
Azure OpenAI Studio
Azure OpenAI Chat API
Azure OpenAI Playground
Azure OpenAI Completion API
GPT-3.5-Turbo

Data sets from the industry

Work on Industry-Relevant Projects

Find below an indicative list of hands-on projects during the course of the program

Product Feedback Review & Sentiment Analysis

The objective of this project is to 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.

Aspect-based Classification for Sentiment Analysis

The objective of this project 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.

Meet Your Faculty and Mentors

Learn from highly skilled professionals in the ML field who have engineered Generative AI solutions across industry verticals & have real-world, hands-on work experience

Dr. Abhinanda Sarkar

Faculty Director, Great Learning

Dr. Abhinanda Sarkar is the Academic Director at Great Learning for Data Science and Machine Learning Programs. Dr. Sarkar received his B.Stat. and M.Stat. degrees from the Indian Statistical Institute (ISI) and a Ph.D. in Statistics from Stanford University. He has taught applied mathematics at the Massachusetts Institute of Technology (MIT); been on the research staff at IBM; led Quality, Engineering Development, and Analytics functions at General Electric (GE); served as Associate Dean at the MYRA School of Business; and co-founded OmiX Labs. Dr. Sarkar’s publications, patents, and technical leadership have been in applying probabilistic models, statistical data analysis, and machine learning to diverse areas such as experimental physics, computer vision, text mining, wireless networks, e-commerce, credit risk, retail finance, engineering reliability, renewable energy, and infectious diseases, His teaching has mostly been on statistical theory, methods, and algorithms; together with application topics such as financial modeling, quality management, and data mining. Dr. Sarkar is a certified Master Black Belt in Lean Six Sigma and Design for Six Sigma. He has been visiting faculty at Stanford and ISI and continues to teach at the Indian Institute of Management (IIM-Bangalore) and the Indian Institute of Science (IISc). Over the years, he has designed and conducted numerous corporate training sessions for technology and business professionals. He is a recipient of the ISI Alumni Association Medal, IBM Invention Achievement Awards, and the Radhakrishan Mentor Award from GE India

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Connor Hagen

Lead Architect, Microsoft Azure OpenAI & AI Co-Innovation Labs
Dr. Pavankumar Gurazada

Faculty - Business and AI, Great Learning

 After graduating from IIT Bombay with a five year integrated Masters degree in Science, Pavan earned his MBA from IIM Bangalore. After his MBA, Pavan joined Alghanim Retail in Kuwait and was instrumental in growing the nascent retail presence of the company. After a two year stint in the retail division, he worked as the B2B sales manager for Saint Gobain in UAE & Oman for 11 years, where he managed the company's distribution network. During his tenure at Saint Gobain, he increased the company's new product sales by 40% and expanded overall sales by 15%. A PhD graduate from IIM Lucknow, Pavan's research focuses on using machine learning methods to understand consumer engagement on social media. His work has been presented in several reputed conferences (EMAC conference 2018, Glasgow; China Internet+ Innovation and Entrepreneurship conference 2019, Hangzhou; NASMEI conference 2019, Chennai and MRSI conference 2019, Mumbai to name a few). His book - Marketing Analytics - published by Oxford University Press is slated for a release later in 2020. Pavan is currently a faculty of Business and AI with Great Learning. He is also an advisor (data science) and a member of the board on a deep tech startup Constems AI that is focused on building computer vision systems for Industry 4.0. His LinkedIn profile is https://www.linkedin.com/in/pavankumar-gurazada-00791312/

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Bradford Tuckfield

Founder, KMBARA
Vinicio DeSola

Senior Data Scientist, Aspen Capital