4 min readJun 28, 2020

Azure Engineer to ⏩ Azure AI Engineer

As per Wikipedia

“In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals.”

In this article I will provide some insights about AI Services provided by Azure platform.

If you are full stack developer or Azure Engineer then you are already familiar with Data Processing/Data Managing/Azure DevOps concepts and workflows.

As Azure AI Engineer ,it is not required to be a expert data scientist but it is always good to understand logic behind Data Modeling .

In world of AI,we will deal with Data Model which is main for Predictions

If you don’t want to create new models and want to use Pre-defined models then Azure is providing Cognitive Services for this purpose.

Cognitive Services & Azure Machine Learning

Microsoft Azure platform provides different set of tools to create /manage & train data.

Refer this slide for more info about this workflow

https://www.slideshare.net/jamserra/azure-data-platform-overview-136859821

1. Pre-Built AI

Azure Cognitive Services

As per Official Docs “Cognitive Services bring AI within reach of every developer — without requiring machine-learning expertise. All it takes is an API call to embed the ability to see, hear, speak, search, understand, and accelerate decision-making into your apps.”

As definition mentioned you don’t need to have any experience with machine learning .As long as you have good understanding about Azure platform & Rest API concepts will be sufficient to start working on Azure Cognitive Services.

1.Vision
Recognize, identify, caption, index, and moderate your pictures, videos, and digital ink content.

2.Language
Allow your apps to process natural language with pre-built scripts, evaluate sentiment and learn how to recognize what users want

3.Speech
Convert speech into text and text into natural-sounding speech. Translate from one language to another and enable speaker verification and recognition.

4.Decision
Build apps that surface recommendations for informed and efficient decision-making.

5.Search
Add Bing Search APIs to your apps and harness the ability to comb billions of webpages, images, videos, and news with a single API call.

6.Cognitive Service Containers
Container support in Azure Cognitive Services allows developers to use the same rich APIs that are available in Azure, and enables flexibility in where to deploy and host the services that come with Docker container

Custom AI

If you want to customize your model with own requirements then azure will help you to create/Train & Deploy Model .

Below are core components for Custom AI in Azure:

Azure Machine Learning Studio

Azure Machine Learning Studio is web-based integrated development environment (IDE) for developing data experiments.

It is like rest of Azure’s cloud services and that simplifies development and deployment of machine learning models and service

Collect Data & Data Processing

Azure Data Factory

Azure Data Factory is Azure’s cloud ETL service for scale-out serverless data integration and data transformation. It offers a code-free UI for intuitive authoring and single-pane-of-glass monitoring and management. You can also lift and shift existing SSIS packages to Azure and run them with full compatibility in ADF

Azure Data Bricks

Azure Databricks is an Apache Spark-based analytics platform optimized for the Microsoft Azure cloud services platform. Designed with the founders of Apache Spark, Databricks is integrated with Azure to provide one-click setup, streamlined workflows, and an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts.

Model Hosting

Azure Kubernetes Service (AKS)

Azure Kubernetes Service (AKS) makes it simple to deploy a managed Kubernetes cluster in Azure. AKS reduces the complexity and operational overhead of managing Kubernetes by offloading much of that responsibility to Azure

Deployment

MLOps

MLOps, or DevOps for machine learning, enables data science and IT teams to collaborate and increase the pace of model development and deployment via monitoring, validation, and governance of machine learning models.

Create reproducible ML pipelines.

Register, package, and deploy models from anywhere.

Capture the governance data for the end-to-end ML lifecycle.

Notify and alert on events in the ML lifecycle.

Monitor ML applications for operational and ML-related issues.

Automate the end-to-end ML lifecycle with Azure Machine Learning and Azure Pipelines.

Automated ML

Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time consuming, iterative tasks of machine learning model development