Analyze Solution Requirements
Lecture
Becky Isserman
Intermediate
1 h 35 m
2019-10-10
Lecture Overview
This is the first course of the AI-100 Exam Preparation Learning Path. In this course, we will discuss all the different APIs available and appropriate use cases for each API,we will discuss the various tools, technologies, and processes available to secure your AI data, andwe will discuss the various analytics solutions, storage solutions, and other services available to create an end to end solution.

Related Learning Path(s):
AI - 100 Designing and Implementing an Azure AI Solution
Objectives
  • Understand the different APIs available and appropriate use cases for each API
  • Understand the various tools, technologies, and processes available to secure your AI data
  • Understand the various analytics solutions, storage solutions, and other services available to create an end to end solution
Lecture Modules
There are a series of Cognitive Services from Vision API, Speech API, Search API, Decision API, and Language API that help when architecting AI Solutions.  In this module, we will discuss all the different APIs available and appropriate use cases for each API.
An introduction to Cognitive Services and discussion on each category of API, so that the user gains an understanding of the different API category types and why Cognitive Services is broken into these API types.
This section will discuss the different Vision APIs available as they relate to use cases for image classification and OCR recognition.
This section will discuss the Speech APIs as they convert speech into text to identify, translate, and create transcripts.
This section will discuss the Search APIs and how customs search can be used in real world scenarios.
This section will discuss the Decision APIs to help modify content and detect anomalies in real world scenarios.
This section will discuss how the Language APIs can help understand written text to help with scenarios related to emotion, reading, and translation in real world scenarios.
AI solutions require data compliance and security processes to be followed, so that data is not breached.  In this module, we will discuss the various tools, technologies, and processes available to secure your AI data.
In this section, we will discuss the various data privacy compliance options available and how to conform your solutions to these different privacy acts.
In this section, we will discuss the security processes that must be followed to conform your data to the compliance and privacy acts, such as HIPAA and GDPR, so that your data meets security standards.
In this section, we will discuss the tools that map to the various processes the privacy compliance acts, so that your data is secure and meets standards.
In this section, we will discuss designing authentication and authorization for your AI solutions, so that you create a minimum security risk.
AI solutions sometimes have an application component or require analytics and other services to create a full end-to-end AI solution.  In this module, we will discuss the various analytics solutions, storage solutions, and other services available to create an end to end solution.
In this section, we will identify a couple of scenarios that integrate with Cognitive Services and AI solutions to address the various components needed for these solutions.
In this section, we will discuss creating a conversational chat bot for hotel reservations in Azure and the various components needed for this chat bot.  We will discuss the scenario starting from a mobile app service all the way through App Insights and why we would use these various components.
In this section, we will discuss image classification for insurance claims, where we start with a web application that ties into the Computer Vision API and stores data within Cosmos DB.  We will discuss why we need to use various components and the business use case for these components.
In this section, we will discuss a recommendations engine for videos that pulls in user data from Blob Storage and processes that data using Azure Machine Learning Services.  In the end the solution is fed into a container application reading the data from Azure Cosmos DB back to the user, so that they can receive video recommendations.
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