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R9 700,00
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Introduction:
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.
Audience profile:
Software engineers concerned with building, managing and deploying AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. They are familiar with C# or Python and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and conversational AI solutions on Azure.
Job role: AI Engineer
Pre-requisites:
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 programming semantics
To gain C# or Python skills, complete the free Take your first steps with C# or Take your first steps with Python learning path before attending the course.
If you are new to artificial intelligence, and want an overview of AI capabilities on Azure, consider completing the Azure AI Fundamentals certification before taking this one.
Course Objectives:
- Describe considerations for AI-enabled application development
- Create, configure, deploy, and secure Azure Cognitive Services
- Develop applications that analyze text
- Develop speech-enabled applications
- Create applications with natural language understanding capabilities
- Create QnA applications
- Create conversational solutions with bots
- Use computer vision services to analyze images and videos
- Create custom computer vision models
- Develop applications that detect, analyze, and recognize faces
- Develop applications that read and process text in images and documents
- Create intelligent search solutions for knowledge mining
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: |
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After completing this module, students will be able to: |
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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: |
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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 |
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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: |
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Lab: Translate Text
Lab: Analyze Text |
After completing this module, you will be able to: |
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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: |
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Lab: Recognize and Synthesize Speech
Lab: Translate Speech |
After completing this module, students will be able to: |
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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: |
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Lab: Create a Language Understanding Client Application
Lab: Create a Language Understanding App Lab: Use the Speech and Language Understanding Services |
After completing this module, students will be able to: |
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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: |
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Lab: Create a QnA Solution |
After completing this module, students will be able to: |
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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: |
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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: |
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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: |
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Lab: Analyze Video
Lab: Analyze Images with Computer Vision |
After completing this module, students will be able to: |
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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: |
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Lab: Classify Images with Custom Vision
Lab: Detect Objects in Images with Custom Vision |
After completing this module, students will be able to: |
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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: |
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Lab: Detect, Analyze, and Recognize Faces |
After completing this module, students will be able to: |
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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: |
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Lab: Read Text in Images
Lab: Extract Data from Forms |
After completing this module, students will be able to: |
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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: |
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Lab: Create a Custom Skill for Azure Cognitive Search
Lab: Create an Azure Cognitive Search solution Lab: Create a Knowledge Store with Azure Cognitive Search |
After completing this module, students will be able to: |
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Torque IT specializes in providing our Clients with Vendor authorized instructor-led training, enablement IT courses, and certification solutions.
Associated certifications and exam:
This course prepares students to write Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution.
On successful completion of this course students will receive a Torque IT attendance certificate.
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