Building Intelligent Web Applications with Azure OpenAI

Learning Objectives

Our engaging instructors and mentors are highly experienced practitioners who bring years of current, modern "on-the-job" modern applied data science, AI and machine learning experience into every classroom and hands-on project. Our instructors are Certified Microsoft Data Scientists. 

Working in a hands-on lab environment led by our expert instructor, you’ll explore

• Design patterns for developing Azure OpenAI web solutions
• Building applications for the intelligent web using Azure OpenAI
• Extracting structure from data: clustering and transforming your data
• Recommending relevant content
• Classification: placing things where they belong
• Relevant Case Study: click prediction for online advertising
• Making the right Machine Learning choices for your web apps
• The future of the intelligent web and Azure AI

Audience

This foundation level course is geared for experienced technical professionals eager to meld the capabilities of AI with the dynamism of web applications.  Roles might include developers, architects, and data scientists who want to learn algorithms that capture, store, and structure data streams coming from the web and web applications, as well as recommendation engines and dive into classification via statistical algorithms, neural networks, and deep learning.

Pre-Requisites

To ensure a smooth learning experience and maximize the benefits of attending this course, you should have the following prerequisite skills:

• Prior programming or scripting experience. Labs are Python-centric, so Python experience would be the most beneficial.  Attendees without scripting experience would be able to watch and follow along with the hands-on portion of the training.
• Basic web development experience is recommended (working with HTML5 / CSS3, etc.)
• Comfort with elementary data concepts, such as databases, data structures, and basic data manipulation.

Take Before: Students should have incoming practical skills aligned with those in the course(s) below, or should have attended the following course(s) as a pre-requisite:

TTPS4872 Python Primer for Data Science and Machine Learning (2 days)

1. Getting Started: Building applications for the intelligent web with Azure OpenAI

• An intelligent algorithm in action
• The intelligent-algorithm lifecycle
• Further examples of intelligent algorithms
• Things that intelligent applications are not
• Classes of intelligent algorithm
• Evaluating the performance of intelligent algorithms
• Important notes about intelligent algorithms

2. Extracting structure from data: clustering and transforming your data

• Data, structure, bias, and noise
• The curse of dimensionality
• K-means
• The relationship between k-means and GMM
• Transforming the data axis

3. Recommending relevant content

• Setting the scene: an online movie store
• Distance and similarity
• How do recommendation engines work?
• User-based collaborative filtering
• Model-based recommendation using singular value decomposition
• The Netflix Prize
• Evaluating your recommender

4. Classification: placing things where they belong

• The need for classification
• An overview of classifiers
• algorithms
• Fraud detection with logistic regression
• Are your results credible?
• Classification with very large datasets

5. Case study: click prediction for online advertising

• History and background
• The exchange
• What is a bidder?
• What is a decisioning engine?
• Click prediction with Vowpal Wabbit
• Complexities of building a decisioning engine
• The future of real-time prediction

6. Developing APIs from Azure OpenAI

• An intuitive approach to building application logic
• Flask APIs and Microservices
• Creating Business Use Cases for Generative AI and Azure OpenAI

7. Building an End-to-End GPT based Web Application

• External API Layer
• Microservices / Web Services Layer + OpenAI + Azure AI
• Business Process Flow Layer (VoiceFlow)
• Front End Layer
• Testing

8. The future of the intelligent web

• Future applications of the intelligent web
• Social implications of the intelligent web