AI-102: Designing and Implementing a Microsoft Azure AI Solution

$2,250.00

Duration: 4 Days
Mode of Delivery: Online -Instructor-led training
Job role: Data Scientists, Data architects
Preparation for exam: AI-102
Cost: USD$2,250.00

AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to design and implement an Azure AI solution. It is intended for software developers wanting to build AI infused applications that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. In this course, you will learn how to develop AI solutions on Azure, using:
• Azure Cognitive Services
• Azure Bot Service
• Azure Cognitive Search

 

19 in stock (can be backordered)

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Audience

AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. The course will use C# or Python as the programming language. Software engineers are concerned with building, managing and deploying AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. They should be familiar with the technology framework using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and conversational AI solutions on Azure. Ideal for software engineers concerned with building, managing and deploying AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. Familiarity with C# or Python and REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and conversational AI solutions on Azure are pre-requisites for this course.

Prerequisites

Before attending this course, students must have:
• Knowledge of Microsoft Azure and ability to navigate the Azure portal
• Knowledge of either C# or Python
• Familiarity with JSON and REST coding semantics
To gain C# or Python skills, complete the first steps with C# and Python coding before attending the course. If you are new to artificial intelligence, and want an overview of AI capabilities on Azure, consider completing the AI-900 Azure AI Fundamentals certification before taking this one.

Skills Gained

After completing this course, students will be able to:
• Plan and manage an Azure Cognitive Services solution
• Implement Computer Vision solutions
• Implement natural language processing solutions
• Implement knowledge mining solutions
• Implement conversational AI solutions

Course outline

Module 1: Introduction to AI on Azure
Artificial Intelligence (AI) is increasingly at the core of modern apps and services. In this module, you’ll learn about some common AI capabilities that you can leverage in your apps, and how those capabilities are implemented in Microsoft Azure. You’ll also learn about some considerations for designing and implementing AI solutions responsibly.
Lessons
• Introduction to Artificial Intelligence
• Artificial Intelligence in Azure
After completing this module, students will be able to:
• Describe considerations for creating AI-enabled applications
• Identify Azure services for AI application development

Module 2: Developing AI Apps with Cognitive Services
Cognitive Services are the core building blocks for integrating AI capabilities into your apps. In this module, you’ll learn how to provision, secure, monitor, and deploy cognitive services.
Lessons
• Getting Started with Cognitive Services
• Using Cognitive Services for Enterprise Applications
Lab: Get Started with Cognitive Services
Lab: Manage Cognitive Services Security
Lab: Monitor Cognitive Services
Lab: Use a Cognitive Services Container
After completing this module, students will be able to:
• Provision and consume cognitive services in Azure
• Manage cognitive services security
• Monitor cognitive services
• Use a cognitive services container

Module 3: Getting Started with Natural Language Processing
Natural Language processing (NLP) is a branch of artificial intelligence that deals with extracting insights from written or spoken language. In this module, you’ll learn how to use cognitive services to analyze and translate text.
Lessons
• Analyzing Text
• Translating Text
Lab: Analyze Text
Lab: Translate Text
After completing this module, students will be able to:
• Use the Text Analytics cognitive service to analyze text
• Use the Translator cognitive service to translate text

Module 4: Building Speech-Enabled Applications
Many modern apps and services accept spoken input and can respond by synthesizing text. In this module, you’ll continue your exploration of natural language processing capabilities by learning how to build speech-enabled applications.
Lessons
• Speech Recognition and Synthesis
• Speech Translation
Lab: Recognize and Synthesize Speech
Lab: Translate Speech
After completing this module, students will be able to:
• Use the Speech cognitive service to recognize and synthesize speech
• Use the Speech cognitive service to translate speech

Module 5: Creating Language Understanding Solutions
To build an application that can intelligently understand and respond to natural language input, you must define and train a model for language understanding. In this module, you’ll learn how to use the Language Understanding service to create an app that can identify user intent from natural language input.
Lessons
• Creating a Language Understanding App
• Publishing and Using a Language Understanding App
• Using Language Understanding with Speech
Lab: Create a Language Understanding App
Lab: Create a Language Understanding Client Application
Lab: Use the Speech and Language Understanding Services
After completing this module, students will be able to:
• Create a Language Understanding app
• Create a client application for Language Understanding
• Integrate Language Understanding and Speech

Module 6: Building a QnA Solution
One of the most common kinds of interaction between users and AI software agents is for users to submit questions in natural language, and for the AI agent to respond intelligently with an appropriate answer. In this module, you’ll explore how the QnA Maker service enables the development of this kind of solution.
Lessons
• Creating a QnA Knowledge Base
• Publishing and Using a QnA Knowledge Base
Lab: Create a QnA Solution
After completing this module, students will be able to:
• Use QnA Maker to create a knowledge base
• Use a QnA knowledge base in an app or bot

Module 7: Conversational AI and the Azure Bot Service
Bots are the basis for an increasingly common kind of AI application in which users engage in conversations with AI agents, often as they would with a human agent. In this module, you’ll explore the Microsoft Bot Framework and the Azure Bot Service, which together provide a platform for creating and delivering conversational experiences.
Lessons
• Bot Basics
• Implementing a Conversational Bot
Lab: Create a Bot with the Bot Framework SDK
Lab: Create a Bot with Bot Framework Composer
After completing this module, students will be able to:
• Use the Bot Framework SDK to create a bot
• Use the Bot Framework Composer to create a bot

Module 8: Getting Started with Computer Vision
Computer vision is an area of artificial intelligence in which software applications interpret visual input from images or video. In this module, you’ll start your exploration of computer vision by learning how to use cognitive services to analyze images and video.
Lessons
• Analyzing Images
• Analyzing Videos
Lab: Analyze Images with Computer Vision
Lab: Analyze Video with Video Indexer
After completing this module, students will be able to:
• Use the Computer Vision service to analyze images
• Use Video Indexer to analyze videos

Module 9: Developing Custom Vision Solutions
While there are many scenarios where pre-defined general computer vision capabilities can be useful, sometimes you need to train a custom model with your own visual data. In this module, you’ll explore the Custom Vision service, and how to use it to create custom image classification and object detection models.
Lessons
• Image Classification
• Object Detection
Lab: Classify Images with Custom Vision
Lab: Detect Objects in Images with Custom Vision
After completing this module, students will be able to:
• Use the Custom Vision service to implement image classification
• Use the Custom Vision service to implement object detection

Module 10: Detecting, Analyzing, and Recognizing Faces
Facial detection, analysis, and recognition are common computer vision scenarios. In this module, you’ll explore the user of cognitive services to identify human faces.
Lessons
• Detecting Faces with the Computer Vision Service
• Using the Face Service
Lab: Detect, Analyze, and Recognize Faces
After completing this module, students will be able to:
• Detect faces with the Computer Vision service
• Detect, analyze, and recognize faces with the Face service

Module 11: Reading Text in Images and Documents
Optical character recognition (OCR) is another common computer vision scenario, in which software extracts text from images or documents. In this module, you’ll explore cognitive services that can be used to detect and read text in images, documents, and forms.
Lessons
• Reading text with the Computer Vision Service
• Extracting Information from Forms with the Form Recognizer service
Lab: Read Text in Images
Lab: Extract Data from Forms
After completing this module, students will be able to:
• Use the Computer Vision service to read text in images and documents
• Use the Form Recognizer service to extract data from digital forms

Module 12: Creating a Knowledge Mining Solution
Ultimately, many AI scenarios involve intelligently searching for information based on user queries. AI-powered knowledge mining is an increasingly important way to build intelligent search solutions that use AI to extract insights from large repositories of digital data and enable users to find and analyze those insights.
Lessons
• Implementing an Intelligent Search Solution
• Developing Custom Skills for an Enrichment Pipeline
• Creating a Knowledge Store
Lab: Create an Azure Cognitive Search solution
Lab: Create a Custom Skill for Azure Cognitive Search
Lab: Create a Knowledge Store with Azure Cognitive Search
After completing this module, students will be able to:
• Create an intelligent search solution with Azure Cognitive Search
• Implement a custom skill in an Azure Cognitive Search enrichment pipeline
• Use Azure Cognitive Search to create a knowledge store

Schedule

Click on the following link to see the current Course Schedule
Our minimum class-size is 3 for this course.
If there are no scheduled dates for this course, it can be customized to suit the time and skill needs of clients and it can be held online, at a rented location or at your premises.
Click on the following link below to arrange for a custom course: Enquire about a course date

Product Information

Artificial Intelligence (AI) in increasingly prevalent in the software applications we use every day; including digital assistants in our homes and cellphones, automotive technology in the vehicles that take us to work, and smart productivity applications that help us do our jobs when we get there.
So what actually is artificial intelligence?
In general terms, we tend to think of AI as software that exhibits one or more human-like capabilities, including:

  • Visual perception. The ability to use computer vision capabilities to accept, interpret, and process input from images, video streams, and live cameras.
  • Text analysis. The ability to use natural language processing (NLP) to not only “read”, but also extract semantic meaning from text-based data.
  • Speech. The ability to recognize speech as input and synthesize spoken output. The combination of speech capabilities together with the ability to apply NLP analysis of text enables a form of hu­man-compute interaction that’s become known as conversational AI, in which users can interact with AI agents (usually referred to as bots) in much the same way they would with another human.
  • Decision making. The ability to use past experience and learned correlations to assess situations and take appropriate actions. For example, recognizing anomalies in sensor readings and taking automat­ed action to prevent failure or system damage.

These kinds of capabilities are increasingly within the reach of everyday software applications, helping make them more intuitive and useful in a wide variety of scenarios that previously existed only in the realms of science fiction. Artificial intelligence usually builds on machine learning to create software that emulates one or more characteristics of human intelligence.

The table below shows all the technologies and tools used in the course.

TECHNOLOGY DESCRIPTION
Azure Manage subscriptions, create workspaces, create resources, manage services.
Azure Bot Service Managed service purpose-built for bot development.
Azure Machine Learning Automated Machine Learning Manage the machine learning lifecycle.
Azure Machine Learning designer Visually connects datasets and modules on an interactive canvas to create machine learning models.
Cognitive Services Comprehensive family of AI services and cognitive APIs to help you build intelligent apps.
Computer Vision AI service that analyzes content in images.
Custom Vision An AI service and end-to-end platform for applying computer vision to your specific scenario.
Face An AI service that analyzes faces in images.
Language Understanding (LUIS) An AI service that allows users to interact with your applications, bots, and IoT devices by using natural language.
Form Recognizer The AI-powered document extraction service that understands your forms.
Learn Provides interactive, hands-on learning paths, which contain the learning path, course modules, and lessons used in this course.
QnA Maker A cloud-based API service that lets you create a conversational question-and-answer layer over your existing data.
Speech Speech-to-Text, Speech Translation, and Text-to-Speech service feature that translates speech in real time.
Text Analytics AI service that uncovers insights such as sentiment, entities, and key phrases in unstructured text.
Text-to-Speech A Speech service feature that converts text to lifelike speech.
Translator Text AI service for real-time text translation.
Visual Studio Codespaces Cloud-hosted dev environments accessible from anywhere.

 

Additional Information

CANCELLATION POLICY – There is never a fee for cancelling seven business days before a class for any reason. Data Vision Systems reserves the right to cancel any course due to insufficient registration or other extenuating circumstances. Participants will be advised prior to doing so.

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