A Closer Look at the Azure Cloud Portfolio – 1. Essentials

• This article helps you understand essentials of Microsoft Azure services compared to Amazon Web Services (AWS) and Google Cloud Platform (GCP).

• According to the reported quarterly earnings for 2021, Microsoft’s Azure cloud revenue has been observed to, once again, outperform both AWS and Google Cloud combined.
• Azure is also gaining its share of high-profile customers with time. As of now, Azure has almost 80 percent of Fortune 500 companies as its customers. 
• While choosing a public cloud service provider, there are several technical factors to consider beyond pricing. Through this Azure high level overview and related tutorials, you will understand Azure architecture and various Azure products.
• In addition, the blog on AWS vs Azure vs Google Cloud highlights the key differences between AWS, Azure, and GCP.

Table of Contents

1. Azure Cloud Concepts
2. Azure Synapse SQL Pool
3. Azure DevOps Boards
4. Azure ML Studio
5. Azure Cheat Sheets
6. Summary
7. Explore More
8. Infographic

Azure Cloud Concepts

• With 60+ announced regions, more than AWS+GCP, Azure makes it easy to choose the datacenter and regions that are right for you and your customers.

Source: 2023 TomTom

• Azure is the most powerful cloud provider, 5 times cheaper than AWS
• Three-fold Azure architecture: IaaS, PaaS and SaaS

Azure packaged software, IaaS, PaaS, and SaaS:

• Azure subscription, region, tenant, resource group, and Active Directory:

• Azure VMs:

• Azure storage, SQL DB and Data Lakes

• Azure Data Lakes

• Azure data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data, and run different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions.
• A Binary Large OBject (BLOB) is a collection of binary data stored as a single entity in a database management system. Blobs are typically images, audio or other multimedia objects, though sometimes binary executable code is stored as a blob. 
• In computer science, a queue is a collection of entities that are maintained in a sequence and can be modified by the addition of entities at one end of the sequence and the removal of entities from the other end of the sequence. By convention, the end of the sequence at which elements are added is called the back, tail, or rear of the queue, and the end at which elements are removed is called the head or front of the queue, analogously to the words used when people line up to wait for goods or services.

Azure Synapse SQL Pool

• Azure Synapse Analytics is Microsoft’s data warehousing offering on Azure Cloud.  It supports three types of runtimes – SQL Serverless Pool, SQL Dedicated Pool, and Spark Pools. 

• There are three major types of data ingestion approaches that can be used to load data into Synapse: the COPY command, the Bulk Insert, and PolyBase.  
• Polybase is a very performant way to load data from Azure Storage to Azure Synapse. It does require going through a handful of steps:
• Create Azure Data Lake Storage Gen2
• Create Source File and upload it on ADLS container
• Create Azure SQL Pool and Synapse Workspace
• Configure Polybase
• Polybase in Action

Learn more about Polybase here.

Azure DevOps Boards

• Azure Boards is a standalone service within the Azure DevOps suite that helps teams plan, track, and discuss work across the entire software development process.

• Step 1: Create a new project

Capability Maturity Model Integration (CMMI) is a process level improvement training and appraisal program. 

• Step 2: Setting up Azure DevOps projects, creating teams, and inviting team members.

• Step 3: Creating and importing work items in Azure DevOps Boards.
• Step 4: Customizing Azure DevOps Boards and filtering work items.
• Step 5: Exploring Azure DevOps backlogs, and organizing workloads into sprints.

Add new items and divide work into time slots called sprints.

Learn more about Azure DevOps here.

Azure ML Studio

Azure Machine Learning is a family of products which include:

1. Azure Machine Learning platform — It is a fully managed cloud service with graphical studio and visual designer. It supports popular python libraries like pyTorch and scikit-learn. It also supports R, the statistical programming language and supports Azure Jupyter Notebooks (it is Microsoft hosted Jupyter notebook).
2. Azure ML Studio (Classic) — It is the v1 version on the product and now referred as “(Classic)”. It is low code or no-code interactive visual workspace environment for training and deploying machine learning models. Since this is historical version it has its own issues and pain areas such as there is 10-GB training data limit also the model format are proprietary and non-portable.
3. Azure Cognitive Service — These are the set of APIs available as finished SaaS product. It has pre built models for things like emotional and sentimental detection, vision, and speech recognition.
4. SQL Server Machine Learning Services/ ML Server — . The SQL Server Machine Learning Services provides statistical analysis and predictive analytics, supporting both Python and R programming environments for SQL Server databases. So we can essentially build and deploy ML models inside SQL Server. The Microsoft Machine Learning Server is a standalone enterprise server for predictive analysis. We can build and deploy models using pre‑processed data. It’s cross platform, runs on Windows Server and Linux.

Azure Machine Learning Studio (ML Studio) is a graphical, collaborative, drag-and-drop web interface from Microsoft for building machine learning models. It does not require coding knowledge of R or Python, and the cloud interface can be used to build models through ML Studio.

There are six steps to build a machine learning model with Azure ML Studio:

• Step One: Load Data

• Step Two: Prepare Data for Modeling

• Step Three: Create Train and Test Datasets

• Step Four: Build the Model

• Step Five: Score Test Data

• Step Six: Evaluate the Model

Use-Case Example:

1. Preparation Phase

• Create a new experiment from the Azure Machine Learning Studio dashboard.
• Import and explore the Adult Census data before moving on to pre-processing.

2. Data Cleaning

• To account for the missing data, you will substitute all missing values by 0 using the Clean Missing Data module.
• The next step is to use the Select Columns in the Dataset module to exclude irrelevant and redundant columns from the data. This is done to reduce the clutter during analysis.
• Once the final set of features is ready, use the Edit Metatdata module to convert the specific columns from String types to Categorical Feature types.

3. Accounting for Class Imbalance

• The right way is to first create the training and test sets and only upsample the training data.
• By upsampling only on training data, none of the information in the validation data is being used to create synthetic observations. So these results should be generalizable.

4. Training a Two-Class Boosted Decision Tree Model and Hyperparameter Tuning

• Hyperparameter tuning is done using the Tune Model Hyperparameters module.

5. Scoring and Evaluating the Models

• Compare how the two models perform using the Score Model and Evaluate Model modules.
• Use the AOC and ROC metrics to evaluate and diagnose your models.

6. Publishing the Trained Model as a Web Service for Inference.

Azure Cheat Sheets

The Azure cheat sheets were created to give you a summary of the most important Azure services that you should know in order to pass the different Azure certification exams such as the AZ-900 Microsoft Azure Fundamentals and AZ-303 Microsoft Azure Architect Technologies. It’s presented mostly in bullet points to provide you with easy-to-digest and easy-to-remember notes that will help you gain a better understanding of the different Azure services.

Summary

• For organisations to truly reap the maximum benefit from their Microsoft Azure cloud investment, having the right governance framework and processes is absolutely crucial.
• One of the advantages of using Azure DevOps is the ability to use multiple agents within the same pipeline.

• Blue-Green deployment. We touch upon our experiences of implementing Blue-Green on the Azure platform, its benefits, and how we achieved zero downtime deployments using some of the Azure platform capabilities.
• Top Business Advantages of The Microsoft Azure Cloud – On-Site Hardware Is Not Needed, Budget-Friendly Subscription Models, Extreme Availability, Seamless Scalability, Access to Enterprise-Level Development Tools, Extreme Cybersecurity, Multiple Compliance Features, Complement On-site IT infrastructure, Suitable for Businesses of All Sizes.

A quick rundown of what makes the cloud great:

• Flexibility/agility is crucial to 64% of people
• Reduce spending on technology by 70%
• Disaster Recovery to avoid unnecessary downtime
• Implementing automatic software updates
• Cap-Ex reduced to avoid 40% over capacity
• Increased collaboration & communication for a a 400% ROI
• Work from anywhere [even with a 6% pay-cut]
• Updating document control to accommodate users
• Improving security with $3 million cost per breach
• Saving the environment with 30% less consumption

Explore More

• Linux Academy Australia: The Book of Basics
• Azure Cheat Sheets
• Overview of AWS Tech Portfolio 2023
• Cloud-Native Tech Autumn 2022 Fair
• Technology Focus Weekly Update 16 Oct ’22
• Cloud-Native Tech Status Update Q3 2022
• Cybersecurity Summer 2022 Round-Up
• Cloud Tech Trends June 2022
• Cybersecurity Monthly Update
• Brand Architecture: Google vs. Amazon
• PRA
• Newsletter
• Frontiers

Infographic

Microsoft Certifications: Azure

Source: Azure ML-as-a-Service

Source: Coursera

Source: Great learning Academy

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