I just get my  AI-100 Microsoft Azure AI Engineer Associate Certification and it is time now to share my preparation notes for those who are interested to pass the “AI-100 Designing and Implementing an Azure AI Solution” exam and get certified too.

This article is just one another preparation guide to Microsoft exam AI-100 (but probably the most complete). I hope it will be useful 🙂

Even you don’t plan to take the exam, all this content is really interesting to read and understand if you want to discover and improve your knowledge on Artificial Intelligence on Azure. You will find below more than 300 slides, good articles, nice courses and excellent tutorials.

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

Before starting studying, you must know very well what this certification is about and what are the prerequisites.

The topics included in this exam are the following :

• Analyze solution requirements(25-30%)
• Design AI solutions(40-45%)
• Implement and monitor AI solutions(25-30%)

More details : https://www.microsoft.com/en-us/learning/exam-ai-100.aspx

Update (june 2019) Official list of subjects in the AI-100 exam (you must read it !!)
https://query.prod.cms.rt.microsoft.com/cms/api/am/binary/RE36ywa

Module 0 – AI Basics

Different job in AI and Data Science
https://medium.com/hackernoon/top-10-roles-for-your-data-science-team-e7f05d90d961

Module 1 – Analyze solution requirement

Part 1 – Recommend Cognitive Services APIs to meet business requirements

Choosing a Microsoft cognitive services technology
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/cognitive-services

Microsoft Azure Cognitive Services: The Big Picture
https://app.pluralsight.com/library/courses/microsoft-azure-cognitive-services-big-picture

Microsoft Azure Cognitive Services: Custom Vision API (Excellent course by
Andy Butland)
https://app.pluralsight.com/player?course=microsoft-azure-cognitive-services-custom-vision-api

Precision versus Recall
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-understand-automated-ml#precision-recall-chart

Performance measures in Azure ML: Accuracy, Precision, Recall and F1 Score
https://blogs.msdn.microsoft.com/andreasderuiter/2015/02/09/performance-measures-in-azure-ml-accuracy-precision-recall-and-f1-score/

Part 2 – Map security requirements to tools, technologies, and processes

Bot Service Compliance
https://docs.microsoft.com/en-us/azure/bot-service/bot-service-compliance?view=azure-bot-service-4.0

Part 3 – Select the software, services, and storage required to support a solution

Machine learning at scale
https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/machine-learning-at-scale

Designing an Intelligent Edge in Microsoft Azure (By Jared Rhodes)
https://app.pluralsight.com/library/courses/microsoft-azure-intelligent-edge-designing

Azure IoT reference architecture
https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/iot/index

Azure IoT reference architecture (Detailed PDF)
https://aka.ms/iotrefarchitecture

Choosing a real-time message ingestion technology in Azure
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/real-time-ingestion

What are your options for real-time message ingestion?

• Azure Event Hub : https://docs.microsoft.com/en-us/azure/event-hubs/
• Kafka : https://docs.microsoft.com/en-us/azure/hdinsight/kafka/apache-kafka-introduction
• IoT Hub : https://docs.microsoft.com/en-us/azure/iot-hub/

Connecting IoT Devices to Azure: IoT Hub and Event Hubs
https://docs.microsoft.com/en-us/azure/iot-hub/iot-hub-compare-event-hubs

Azure Databricks
https://docs.microsoft.com/en-us/azure/azure-databricks/what-is-azure-databricks

Tutorial: Sentiment analysis on streaming data using Azure Databricks
https://docs.microsoft.com/en-us/azure/azure-databricks/databricks-sentiment-analysis-cognitive-services

Build a real-time recommendation API on Azure
https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/ai/real-time-recommendation

Identify storage required to store logging, bot state data, and Cognitive Services output
https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/ai/conversational-bot

Choose the right data store
https://docs.microsoft.com/en-us/azure/architecture/guide/technology-choices/data-store-overview

Data Warehouse
https://azure.microsoft.com/services/sql-data-warehouse/

Azure Data Lake Analytics
https://docs.microsoft.com/en-us/azure/data-lake-analytics/data-lake-analytics-overview

Azure Data Lake Store
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-introduction

Cosmos DB Global Distribution:
https://docs.microsoft.com/en-us/azure/cosmos-db/distribute-data-globally

==> Microsoft recommend CosmosDB for warm path storage (Warm path storage holds data that must be available immediately from device for reporting and visualization)

Enable edge intelligence with Azure IoT Edge (excellent introduction video to Azure IoT Edge)
https://channel9.msdn.com/events/Connect/2017/T253

Install the Azure IoT Edge runtime on Debian-based Linux system
https://docs.microsoft.com/en-us/azure/iot-edge/how-to-install-iot-edge-linux

Deploy Azure IoT Edge modules from the Azure portal
https://docs.microsoft.com/en-us/azure/iot-edge/how-to-deploy-modules-portal

Creating an image recognition solution with Azure IoT Edge and Azure Cognitive Services
https://dev.to/azure/creating-an-image-recognition-solution-with-azure-iot-edge-and-azure-cognitive-services-4n5i

Microsoft IoT edge built-in modules
https://azuremarketplace.microsoft.com/en-us/marketplace/apps/category/internet-of-things?search=iot&page=1&filters=microsoft

Azure Stream Analytics
https://azure.microsoft.com/en-us/services/stream-analytics/

Another option for stream analytics is Apache Spark in Azure Databrick or HDInsigt, or Apache Storm in HDInsight

Apache Storm
https://intellipaat.com/blog/what-is-apache-storm/
https://docs.microsoft.com/en-us/azure/hdinsight/storm/apache-storm-overview
https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-overview
https://docs.microsoft.com/bs-latn-ba/azure/hdinsight/interactive-query/apache-interactive-query-get-started

Anomaly detection using machine learning in Azure Stream Analytics
https://www.youtube.com/watch?v=Ra8HhBLdzHE

Built-in ML based Anomaly Detection
-> Un-supervised learning models : Learn from the data it sees

Anomaly detection in Azure Stream Analytics
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-machine-learning-anomaly-detection

Real-Time ML based Anomaly Detection in Azure Stream Analytics (same as above but demo with Raspberry Pi)
https://www.youtube.com/watch?v=qWxdL5gngaA

Anomaly Generator (Git)
https://aka.ms/ASAanomalyGenerator

Performing sentiment analysis by using Azure Stream Analytics and Azure Machine Learning Studio
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-machine-learning-integration-tutorial

Real-time analytics on IoT Edge with Azure Stream Analytics
https://sec.ch9.ms/ch9/6959/5b904422-823a-49d7-8639-d5fbc80e6959/CONN17T157_high.mp4

Azure Stream Analytics now available on IoT Edge
http://aka.ms/ASAEdge

Module 2 – Design AI Solutions (40-45%)

Part 1 – Design solutions that include one or more pipelines

What is Azure Machine Learning
https://docs.microsoft.com/en-us/azure/machine-learning/service/overview-what-is-azure-ml

Tutorial: Predict automobile price with the visual interface
https://docs.microsoft.com/en-us/azure/machine-learning/service/ui-tutorial-automobile-price-train-score

Create and manage Azure Machine Learning service workspaces
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace

AI Pipelines
https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines
https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-azure-machine-learning-architecture#pipeline

1-Import and clean data
2-Train a machine learning model
3-Score and evaluate a model

4- Deploy the trained model

Azure Machine Learning Pipeline
https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/machine-learning-pipelines

AML Samples:
https://github.com/Azure/MachineLearningNotebooks

Get Started with Azure Machine Learning
https://www.youtube.com/watch?v=GBDSBInvz08

How to use Notebooks with Azure Machine Learning workspace
https://www.youtube.com/watch?v=lCkYUHV86Mk

Choose a Compute target
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where#choose-a-compute-target

Deploy a model to an Azure Kubernetes Service cluster
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-azure-kubernetes-service

Deploy a model using a custom Docker image
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-custom-docker-image

Azure Data Factory
https://docs.microsoft.com/en-in/azure/data-factory/introduction

Data Science Virtual Machine :

• Data Science Virtual Machine : https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview
• Geo AI Data Science Virtual Machine : https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/geo-ai-dsvm-overview
• Deep Learning Virtual Machine

Part 2 – Design solutions that uses Cognitive Services

Cognitives Services Directory
https://azure.microsoft.com/en-gb/services/cognitive-services/directory/

Process images with the Computer Vision service (course 32 minutes with sandbox test environment + 3 knowledge questions)
https://docs.microsoft.com/en-us/learn/modules/create-computer-vision-service-to-classify-images/index

Classify images with the Microsoft Custom Vision Service (course 40 minutes)
https://docs.microsoft.com/en-us/learn/modules/classify-images-with-custom-vision-service/index

Develop solutions by using intelligent algorithms related to speech, natural language processing, Bing Search, and recommendations and decision making

Speech Service Documentation
https://docs.microsoft.com/en-gb/azure/cognitive-services/speech-service/
https://docs.microsoft.com/en-gb/azure/cognitive-services/speech-service/overview

Bing Web Search API Documentation
https://docs.microsoft.com/en-us/azure/cognitive-services/bing-web-search/
https://docs.microsoft.com/en-us/azure/cognitive-services/bing-web-search/overview

What is Custom Decision Service?
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-decision-service/custom-decision-service-overview

Sample showing how to deploy a AI model from the Custom Vision service to a Raspberry Pi 3 device using Azure IoT Edge
https://github.com/Azure-Samples/Custom-vision-service-iot-edge-raspberry-pi

Part 3 – Design solutions that implement the Bot Framework

Building a bot
https://docs.microsoft.com/en-us/azure/bot-service/bot-service-overview-introduction?view=azure-bot-service-4.0

Bot Framework Emulator
https://github.com/microsoft/BotFramework-Emulator

https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-howto-v4-luis?view=azure-bot-service-4.0&tabs=cs

https://www.qnamaker.ai

https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-howto-qna?view=azure-bot-service-4.0&tabs=cs/

https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/tutorials/create-publish-query-in-portal

Data sources for QnA Maker content
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/data-sources-supported

Best practices of a QnA Maker knowledge base
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/best-practices

LUIS (Language Understanding Intelligent Services)
https://www.luis.ai/

Entity types and their purposes in LUIS
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-concept-entity-types

Composite entity
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/reference-entity-composite

List entity
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/reference-entity-list

Enterprise-grade conversational bot
https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/ai/conversational-bot

Part 4 – Design the compute infrastructure to support a solution.

What are field-programmable gate arrays (FPGA)
https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-accelerate-with-fpgas

Inside the Microsoft FPGA-based configurable cloud
https://channel9.msdn.com/Events/Build/2017/B8063

Hyperscale hardware: ML at scale on top of Azure + FPGA
https://channel9.msdn.com/events/Build/2018/BRK3202

GPUs vs CPUs for deployment of deep learning models by Fidan Boylu Uz
https://azure.microsoft.com/en-us/blog/gpus-vs-cpus-for-deployment-of-deep-learning-models/

Reference architecture: Machine Learning model training with AKS
https://azure.microsoft.com/en-us/solutions/architecture/machine-learning-with-aks/

Deploy Deep Learning CNN on Kubernetes Cluster with GPUs – AML version
https://github.com/Microsoft/AKSDeploymentTutorialAML

Deploying Deep Learning Models on Kubernetes with GPUs
https://blogs.technet.microsoft.com/machinelearning/2018/04/19/deploying-deep-learning-models-on-kubernetes-with-gpus/

Part 5 – Design Data for Governance, compliance, integrity & security

Managing GDPR compliance on Azure BRK2091
https://www.youtube.com/watch?v=oF6d8fNqT3I

Module 3 – Implement and monitor AI solutions (25-30%)

Part 1 – Implement and AI Workflow

IoT Hub
AML Services
AML Sample
https://github.com/Azure/MachineLearningNotebooks

VS Code extension for Azure Machine Learning
https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-ai

Part 2 – Integrate AI services with solution components

Azure IoT reference architecture
https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/iot/index

Add authentication to your bot via Azure Bot Service
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-authentication?view=azure-bot-service-4.0&tabs=aadv1%2Ccsharp%2Cbot-oauth

Part 3 – Monitor and Evaluate the AI environment

Overview of alerts in Microsoft Azure
https://docs.microsoft.com/en-us/azure/azure-monitor/platform/alerts-overview

Monitor your Azure Machine Learning models with Application Insights
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-enable-app-insights

ML Processing Pipeline : Ingest -> Storage -> Analyze -> Interact

Add telemetry to your bot
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-telemetry?view=azure-bot-service-4.0

Bot analytics
https://docs.microsoft.com/en-us/azure/bot-service/bot-service-manage-analytics?view=azure-bot-service-4.0

Real-Time Analytics (architecture)
Image Classification (architecture)
Interactive Bot (architecture)

Azure AutoML
Hyperdrive ==> automates the tuning of hyperparameters by defining how to tweak each parameter and criteria and adding stopping conditions

Understand automated machine learning results
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-understand-automated-ml

Other useful materials

Developing AI Models in Microsoft Azure (Excellent course by Sahil Malik)
https://app.pluralsight.com/player?course=microsoft-azure-ai-models-developing

Managing Microsoft Azure AI Solutions (Another excellent course by Sahil Malik)
https://app.pluralsight.com/library/courses/microsoft-azure-ai-solutions-managing

Microsoft Azure Developer: Creating and Integrating AI with Azure Services (Again an excellent course by Sahil Malik)
https://app.pluralsight.com/player?course=microsoft-azure-developer-creating-integrating-ai-services-update

AI-100: Designing and Implementing an Azure AI Solution — Study Guide
https://medium.com/@marioamendieta/ai-100-designing-and-implementing-an-azure-ai-solution-study-guide-f0065db01c83

MLOps Workshop
https://github.com/microsoft/MCW-ML-Ops

Microsoft Cloud Workshop library
https://microsoftcloudworkshop.com/

Creating an image recognition solution with Azure IoT Edge and Azure Cognitive Services
https://dev.to/azure/creating-an-image-recognition-solution-with-azure-iot-edge-and-azure-cognitive-services-4n5i

Be prepared for questions on :

• Azure Cognitives Services (a lot of questions)
• Azure AI + AKS + ACI
• Azure IoT Edge + Azure Stack + Azure DataBox Edge
• IoT Hub + EventBus
• Azure Storage
• Azure Stream Analytics
• Azure Functions + Azure Logic App
• LUIS
• Monitoring Apps & AI applications
• Azure Bot Service + QnA Maker + Cognitives Services
• Security (Authentication, Azure Key Vault)

Hope this study guide will be useful for you. Don’t hesitate to share, or post a comment or send me a message on Twitter @squastana or on LinkedIn
https://www.linkedin.com/in/stanislasquastana/

Last but not least, don’t forget to spend time on http://microsoft.com/learn where you can find additional materials to prepare your certification.

— Stanislas Quastana —