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Azure AI Solution Requirements

Course

Intro Video

Photo of Dan Sasse

Dan Sasse

Azure Training Architect II

Length

02:05:58

Difficulty

Advanced

Videos

15

Hands-on Labs

2

Course Details

Artificial Intelligence, or AI, and the Machine Learning that drives it, is one of the most exciting new frontiers of technology being presented in the Cloud today. Microsoft's AI-100 is a certification path and exam that covers the Azure AI services and products available.

The Certification, as described by Microsoft:

Candidates for this exam analyze the requirements for AI solutions, recommend appropriate tools and technologies, and implements solutions that meet scalability and performance requirements.

Candidates translate the vision from solution architects and work with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end solutions. Candidates design and implement AI apps and agents that use Microsoft Azure Cognitive Services and Azure Bot Service. Candidates can recommend solutions that use open source technologies.

Candidates understand the components that make up the Azure AI portfolio and the available data storage options.

Candidates implement AI solutions that use Cognitive Services, Azure bots, Azure Search, and data storage in Azure. Candidates understand when a custom API should be developed to meet specific requirements.

This first course covers the prerequisites of planning and designing an AI solution.

Syllabus

Course Introduction

Introduction

Course Introduction

00:06:19

Lesson Description:

This video discusses the who and what of the AI-100 Exam Test Taker, as well as reviewing what this course covers. Note from the Training Architect: This course is not lengthy in and of itself. However, an exam taker looking to maximize their time who also feels fairly comfortabile with Azure AI Services is encouraged to jump straight to Course 5. In that course, we review all of the different segments and technologies covered on the exam. This can ensure that time is spent only on learning what needs to be learned and not potentially reviewing material that doesn't need to be reviewed.

About the Training Architect

00:02:03

Lesson Description:

Meet the Training Architect, Dan Sasse. https://www.linkedin.com/in/danielsasse/

Analyze Solution Requirements

Recommend Cognitive Services APIs to meet Business Requirements

Select the Processing Architecture for a Solution

00:04:47

Lesson Description:

In this lesson, we discuss the initial considerations an engineer tasked with recommending a cognitive service must deliberate: selecting the processing architecture. We discuss application versus bot-based cognitive solutions and introduce the five categories of services. We also briefly review what machine learning is and where cognitive services fits in relation to it in the Microsoft catalog of Azure services. Further reading documentation: https://docs.microsoft.com/en-us/azure/cognitive-services/ https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/data-science-and-machine-learning

Select the Appropriate Data Processing Technologies

00:02:43

Lesson Description:

In this lesson, we provide a brief overview of two specific data processing technologies — Data Analytics and Data Transformation. A review of different database technologies is included, but only as a reference as storage services and applications. They will be covered in much more detail later in the course. Further reading documentation: https://docs.microsoft.com/en-us/azure/storage/common/storage-introduction https://docs.microsoft.com/en-us/azure/architecture/guide/technology-choices/data-store-overview https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-overview-what-is

Select the Appropriate AI Models and Services

00:04:44

Lesson Description:

In this lesson, we touch on each of the AI Cognitive Services APIs Azure has to offer and discuss a few examples along the way. Further reading documentation: https://docs.microsoft.com/en-us/azure/cognitive-services/cognitive-services-and-machine-learning https://docs.microsoft.com/en-us/azure/cognitive-services/welcome

Identify Components and Technologies Required to Connect Service Endpoints

00:03:20

Lesson Description:

In this lesson, we discuss the components of Cognitive Services that support integration and communication with applications and development efforts. In other words — what do we use, and how do we go about putting a Cognitive Service to work. Further reading documentation: https://docs.microsoft.com/en-us/rest/api/azure/ https://docs.microsoft.com/en-us/azure/cognitive-services/cognitive-services-apis-create-account

Identify Automation Requirements

00:02:00

Lesson Description:

This lesson provides an overview of the technologies available to assist automation efforts when designing a Cognitive Services solution. Further reading documentation: https://docs.microsoft.com/en-us/azure/automation/automation-intro https://docs.microsoft.com/en-us/azure/logic-apps/logic-apps-overview https://docs.microsoft.com/en-in/azure/azure-functions/functions-overview

Hands-on Labs are real live environments that put you in a real scenario to practice what you have learned without any other extra charge or account to manage.

00:30:00

Map Security Requirements to Tools, Technologies, and Processes

Identify Processes and Regulations Needed to Conform with Data Privacy, Protection, and Regulatory Requirements

00:02:06

Lesson Description:

This lesson touches on the regulation and data privacy concerns of designing and implementing an AI/ML solution. Further Reading Documentation: https://docs.microsoft.com/en-us/microsoft-365/compliance/gdpr-dsr-azure https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/govern/policy-compliance/regulatory-compliance

Identify which Users and Groups have Access to Information and Interfaces

00:03:13

Lesson Description:

This lesson is a review of how we identify and restrict what users and groups have access to our AI and Cognitive Services solutions. Further Reading Documentation: https://docs.microsoft.com/en-us/azure/role-based-access-control/built-in-roles https://docs.microsoft.com/en-us/azure/role-based-access-control/overview https://docs.microsoft.com/en-us/azure/key-vault/key-vault-overview

Identify Appropriate Tools for a Solution

00:03:00

Lesson Description:

In this lesson, we discuss the tools that can appropriately report and enforce access, compliance, and security for a well-designed Cognitive Services or AI/ML solution. Further Reading Documentation: https://docs.microsoft.com/en-us/microsoft-365/compliance/compliance-manager-overview https://docs.microsoft.com/en-us/azure/governance/policy/overview https://docs.microsoft.com/en-us/azure/information-protection/what-is-information-protection

Identify Auditing Requirements

00:04:15

Lesson Description:

This lesson is centered around discussing what needs to be considered for auditing, including what that actually is, where we find it, and what tools are available to collect and analyze it. Further Reading Documentation: https://docs.microsoft.com/en-us/azure/security/fundamentals/log-audit https://docs.microsoft.com/en-us/azure/azure-monitor/overview https://docs.microsoft.com/en-us/azure/azure-monitor/log-query/log-query-overview

Select the Software, Services, and Storage Required to Support a Solution

Identify Appropriate Services and Tools for a Solution

00:03:28

Lesson Description:

In this lesson, we discuss the tools needed to develop a custom Machine Learning solution, as well as reviewing two important services, used in parallel, with almost any AI/ML design. Further Reading Documentation: https://docs.microsoft.com/en-us/azure/machine-learning/service/overview-what-is-azure-ml https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/machine-learning-at-scale

Identify Integration Points with other Microsoft Services

00:05:01

Lesson Description:

This lesson is primarily a review of the various 'middlemen' applications we would need to consider when analyzing a solution using AI/ML Azure offerings. Further Reading Documentation: https://docs.microsoft.com/en-us/azure/event-grid/overview https://docs.microsoft.com/en-us/azure/event-hubs/event-hubs-about

Identify Storage Required to Store Logging, Bot State Data, and Cognitive Services Output

00:03:34

Lesson Description:

This lesson centers around three primary storage functions, and highlighting the knowledge needed to ensure a successful certification experience. Further Reading Documentation: https://docs.microsoft.com/en-us/azure/architecture/guide/technology-choices/data-store-comparison https://docs.microsoft.com/en-us/azure/architecture/guide/design-principles/use-the-best-data-store https://docs.microsoft.com/en-us/azure/cosmos-db/introduction

Hands-on Labs are real live environments that put you in a real scenario to practice what you have learned without any other extra charge or account to manage.

00:45:00

What's Next?

Where do you go from here?

Good Work! Now on to the next Course Segment!

00:00:19

Lesson Description:

Well done on completing Course Segment 1, Solution requirements, in our Certification Review of the Azure AI-100. We'd appreciate some feedback before you move on to Segment 2, Components and Services.

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