Designing and Implementing an Azure AI Solution (AI-100)

AI-100: Designing and Implementing an Azure AI Solution

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 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.Enroll & Get Certified now!

✔ Course Duration : 32 hrs

✔ Training Options : Live Online / Self-Paced / Classroom

✔ Certification Pass : Guaranteed

AI-100: Designing and Implementing an Azure AI Solution

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.

What you will Learn

Recommend Cognitive Services APIs to meet business requirements
Map security requirements to tools, technologies, and processes
Select the software, services, and storage required to support a solution
Design solutions that include one or more pipelines
Design solutions that uses Cognitive Services
Design solutions that implement the Bot Framework
Design the compute infrastructure to support a solution
Design for data governance, compliance, integrity, and security
Implement an AI workflow
Integrate AI services with solution components
Monitor and evaluate the AI environment

PREREQUISITES

Azure Fundamentals
Understanding of Azure storage technologies
C# Knowledge

CURRICULUM

Learning Objectives:

Select the processing architecture for a solution
Select the appropriate data processing technologies
Select the appropriate AI models and services
Identify components and technologies required to connect service endpoints
Identify automation requirements

Learning Objectives:

Identify processes and regulations needed to conform with data privacy, protection, and regulatory requirements
Identify which users and groups have access to information and interfaces
Identify appropriate tools for a solution
Identify auditing requirements

Learning Objectives:

Identify appropriate services and tools for a solution
Identify integration points with other Microsoft services
Identify storage required to store logging, bot state data, and Cognitive Services output

Learning Objectives:

Define an AI application workflow process
Design a strategy for ingest and egress data
Design the integration point between multiple workflows and pipelines
Design pipelines that use AI apps
Design pipelines that call Azure Machine Learning models
Select an AI solution that meet cost constraints

Learning Objectives:

Design solutions that use vision, speech, language, knowledge, search, and anomaly detection APIs

Learning Objectives:

Integrate bots and AI solutions
Design bot services that use Language Understanding (LUIS)
Design bots that integrate with channels
Integrate bots with Azure app services and Azure Application Insights

Learning Objectives:

Identify whether to create a GPU, FPGA, or CPU-based solution
Identify whether to use a cloud-based, on-premises, or hybrid compute infrastructure
Select a compute solution that meets cost constraints

Learning Objectives:

Define how users and applications will authenticate to AI services
Design a content moderation strategy for data usage within an AI solution – Ensure that data adheres to compliance requirements defined by your organization
Ensure appropriate governance for data
Design strategies to ensure the solution meets data privacy and industry standard regulations

Learning Objectives:

Develop AI pipelines
Manage the flow of data through solution components
Implement data logging processes
Define and construct interfaces for custom AI services
Integrate AI models with other solution components
Design solution endpoints
Develop streaming solutions

Learning Objectives:

Configure prerequisite components and input datasets to allow consumption of Cognitive Services APIs
Configure integration with Azure Services
Configure prerequisite components to allow connectivity with Bot Framework
Implement Azure Search in a solution

Learning Objectives:

Identify the differences between KPIs, reported metrics, and root causes of the differences
Identify the differences between expected and actual workflow throughput
Maintain the AI solution for continuous improvement
Monitor AI components for availability
Recommend changes to an AI solution based on performance data

FAQs

You can enroll for this classroom training online. Payments can be made using any of the following options and receipt of the same will be issued to the candidate automatically via email.
1. Online ,By deposit the mildain bank account
2. Pay by cash team training center location

Highly qualified and certified instructors with 20+ years of experience deliver more than 200+ classroom training.

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You will never miss a lecture at Mildaintrainigs! You can choose either of the two options: View the recorded session of the class available in your LMS. You can attend the missed session, in any other live batch.

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Yes, you can cancel your enrollment if necessary prior to 3rd session i.e first two sessions will be for your evaluation. We will refund the full amount without deducting any fee for more details check our Refund Policy

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