Azure AI Engineer Associate

Azure AI Engineer Associate




Your current boss is asking you to innovate with AI/ML, your mother wants to know if ML robots are going to put you out of a job, your friends want you to build thinking bots so they don’t have to and you think you know the difference between AI and ML, but not really! Don’t worry, CloudLinear is here to clarify, educate, enhance and get your hands dirty with this course. This is a lab-oriented, hands-on advanced course on Azure Machine Learning, Deep Learning, Cognitive AI and IoT services that will give you the confidence to become a machine learning practitioner. In addition, the course will prepare you to successfully take the Azure AI-100 Exam and become a Microsoft Certified: Azure AI Engineer Associate.

Target Audience: Technical IT Professionals with at least 5 years of industry experience

Class length: 24 hrs

Next Scheduled Class: 2/23/2020

Location:Frisco, TX


1. Who is the target audience for this class?

IT Professionals with at least 5 years of industry experience.

2. How long is the class?

24 hours.

3. How large is the class? 

Class size is limited to a maximum of 8 students to ensure quality instruction.

4. Do I need prior Azure experience to attend this class?
Prior Azure exprience is not required for the course but piror IT industry professional experience is required.

5. Do I need programming experience to attend this class?

No, you do not need to be a programmer to successfully complete the course. But, familiarity with programming so you can understand the provided scripts will greatly help.

6. What do I need to bring to this class?

Please bring a laptop, power supply and a pen/notebook to take notes.

7. How much will usage of all the Azure services cost during class?

We will use all free tier Azure services for the class.

8. Will there be hands-on lab work in this class?

Yes, this is hands-on practical course and we will do labs on each topic to reinforce what we are learning.

9. Will I receive a course completion certificate?

Yes, an electronic course completion certificate will be emailed to you upon request.

10. Can I get a receipt for the course?

Yes, CloudLinear will provide electronic receipts for the class fees.

11. Will the course help me get Azure AI-100 Certified?

Yes, the course is focused on all the material you will need to know successfully pass the Azure AI-100: Designing and Implementing an Azure AI Solution certification.

12. Can I become a ML practitioner in the real world after attending the course?

Yes, with the hands-on practical knowledge you get in the course, you will be able to practice as a high performing machine learning specialist in the real world.

13. Why attend an instructor-led class, when there are so many cheaper online courses?

A structure that gives you the motivation to commit the needed time to master all the required services for the certification. In addition, the discussions and interaction that keeps the class interesting from the perspective of the different professionals attending are irreplaceable.

14. What the instructor’s credentials for this class?

Francis Dogbey is the instructor for this course. Francis Dogbey is a Technology Evangelist and Speaker driving intelligent cloud adoption and IoT Ecosystem architectures including data processing with expertise on designing and selling complex cloud architecture products including: Artificial Intelligence, Internet Of Things, Azure Databricks, Cloud Infrastructure, Big Data architectures, Machine Learning Models, Deep Learning models and parallel computing platforms.
Francis Dogbey is a Microsoft Certified Instructor and AP Practitioner with a passion for teaching hands-on Microsoft Azure Certification courses in domains such as AI, Data Science, IoT, Data Platforms and Kubernetes and imparting skills to students towards Microsoft Certified AI Engineer. Microsoft Certified Data Engineer, Microsoft Certified Data Scientist and Kubernetes CKAD certification.

Francis’ LinkedIn Profile:

Course Outline

Module 1 – Analyze Solution Requirements:

· Recommend Cognitive Services APIs to meet business requirements

· 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

· Map security requirements to tools, technologies, and processes may include but is not limited to

· 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

· Select the software, services, and storage required to support a solution may include but is not limited to:

· 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

Module 2 – Design AI Solutions:

· Design solutions that include one or more pipelines

· 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

· Design solutions that uses Cognitive Services

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

· Design solutions that implement the Bot Framework

· 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

· Design the compute infrastructure to support a solution

· 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

· Design for data governance, compliance, integrity, and security

· 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

Module 3 – Implement and Monitor AI Solutions:

· Implement an AI workflow

· 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

· Integrate AI services with solution components

· 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

· Monitor and evaluate the AI environment

· 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

Capstone Project: Azure Cognitive Search – Knowledge Mining

In this capstone project, you will create an enterprise search solution by applying knowledge mining to business documents like contracts, memos, presentations, images and forms. You will use Microsoft Azure AI technology to extract insights from unstructured data and expose the results in a Bot interface.

At the end of this capstone project, the student will have learned:

· What Azure Cognitive Search is

· How to implement a Cognitive Search Solution

· Why this technology can be useful for any company

· When to use this solution for demos, POCs and other business scenarios The hands-on hack will teach you how to use Microsoft Azure Search combined with Microsoft Cognitive Services for entity recognition, image analysis, text translation, form recognizer and indexed search on enterprise business documents. This approach uses Artificial Intelligence to create an advanced search experience.

In this capstone project students will work in groups and we will cover these key concepts:

· Fundamentals of Azure Search and its capabilities

· Microsoft Cognitive Search and its key business scenarios

· Building an enrichment data pipeline for search using predefined and custom skillsets:

· Text skills like entity recognition, language detection, text manipulation and key phrase extraction

· Image skills like OCR

· Content moderation skills to detect documents with incompliant content

· Use the enriched data for an advanced search experience for business documents within an enterprise.

· Expose the knowledge mining solution using a bot interface for document search and consumption.

· Create custom skill that leverages the Form Recognizer Service to get insights from the form-based data by extracting key-value pairs that describe each form field and the value it contains.


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