3 Critical Aspects of Microsoft Azure Data Science

With companies generating more data than ever, Data Science has become an essential part of their business models.

Companies use Data Science Predictive Models to extract business-focused insights from their data. Data Science helps them to make strategic decisions and boost their business. 

Working with data and creating Data Science Models could be tedious and complex. This is where the Microsoft Azure Data Science platform comes to your aid.

Microsoft Azure Data Science provides you with an environment to prepare and train your Data Science Models. 

As of now, it provides you with 22 different categories of Cloud Services that are useful for the Data Science Model Life Cycle, including Artificial Intelligence (AI), Machine Learning (ML), Blockchain, Networking, Containers, Analytics, Storage, Security, Databases, Compute, etc.

This article provides an overview of Microsoft Azure Data Science and its significance for your business. It also briefs you on some of the most popular Microsoft Azure Data Science Tools and Services.

Table of Contents

• Introduction to Data Science
• Introduction to Microsoft Azure Data Science
• Machine Learning Operations in Microsoft Azure Data Science
• Automated Machine Learning in Microsoft Azure Data Science
• Important Microsoft Azure Data Science Tools and Services
  • Azure Virtual Machine
  • HDInsight Spark Cluster
  • Azure Data Lake
  • Azure Databricks
  • Azure Synapse Analytics
• Conclusion

Introduction to Data Science

Data Science, in simple terms, is the field where Computer Science meets Statistics. We use scientific methods to turn data into values by asking questions, creating hypotheses, and devising experiments. 

These experiments result in conclusions, discoveries, and inventions. Artificial Intelligence and Machine Learning result in Predictive and Prescriptive Models, which help you extract meaningful insights from your data.

The 4 pillars of Data Science are:

• Business/domain knowledge
• Mathematics (particularly statistics and probability)
• Computer science (like data architecture and engineering)
• Communication (both written and verbal)

In an ideal world, these pillars represent the areas in which Data Scientists should be experts. 

Want to learn more about Data Science and its lifecycle? Refer to the Ultimate Guide to Data Science Simplified to learn more.

Introduction to Microsoft Azure Data Science

Microsoft Azure Data Science provides many data science tools and services to Microsoft Data Analysts or Microsoft Azure Data Scientists for easy analysis and development of Predictive Models. 

If you wanted to build Predictive or Prescriptive Models in the Microsoft Azure Data Science platform, you needed to bring together a bunch of different tools and services.

For example, you need to integrate storage tools like Azure Blob Storage or Azure Data Lakes Storage for a single Predictive Model, as you cannot train your models without data.

You can run your code and train your model on Virtual Machines, Spark Cluster, Azure HDInsight, or Azure Databricks. You require Virtual Networks and Azure Key Vault to manage and secure your data for business agility.

Moreover, if you wanted to repeat your experiments using a consistent set of Data Science libraries and the different versions, you needed Docker Containers and Azure Container Registry to store those Docker Containers. 

You were required to put everything inside your Azure Virtual Network (VNet). To run all this at scale, you needed Azure Kubernetes Service inside your VNet.

Doesn’t this sound like a highly complex task to piece everything together and get your model up and running?

Microsoft Azure Data Science Platform helps you with that and removes this complexity. As a managed platform, it comes with its own Compute, hosted Notebooks, and capabilities for Model Management, Version Control, and Model Reproducibility. 

You can also layer that on top of your existing Microsoft Azure services. For example, you can plug in the Compute and storage that you already have and your other infrastructure services.

Microsoft Azure Data Science platform helps you connect them in a single environment so that you can have one end-to-end Modular platform for your entire Data Science Model Life Cycle, which includes:

• Data Preparation: This involves extracting operational data from multiple data sources and cleaning it to build a Predictive Model.
• Building Predictive Models: This involves developing a Predictive Model according to the data you have collected to find meaningful insights.
• Training Models: This involves training or refining the Predictive Model you built earlier by changing the Hyperparameters in every iteration. 
• Package and Deploy Models: This involves Packaging and Deploying your Prediction Model after refining it as much as possible.

Machine Learning Operations in Microsoft Azure Data Science

Microsoft Azure Data Science has an integrated DevOps approach taken care of by Machine Learning Operations (MLOps). 

It makes it easier for Microsoft Azure Data Scientists and Engineers to work together because Data Engineers already understand how Continuous Integration and Continuous Deployment work. Data Scientists know how to train Predictive Models.

So, by enabling them to work together, Microsoft Azure Data Science ensures high-quality models at scale in production. 

With MLOps incorporated as a part of the Microsoft Azure Data Science platform, Data Scientists can create a discrete pipeline for each model. 

MLOps also helps them incorporate reproducibility in the entire Data Science Model Life Cycle, including training, testing, and production environment. Hence, making it easier for them to track and reproduce their model.

Data Science helps train and deploy new Data Science Model versions to ensure that you have the best quality models in production. For example, assume that you have a wind farm that you manage. You want to optimize the energy output and have a predictive maintenance scenario to avoid downtime. 

So, each of your discrete pipelines helps you consistently build, train, package, and deploy Data Science Models to different windmills and iterate as new data comes in.

“Want to check out how Data Engineers use Azure? Refer to Microsoft Azure Data Engineering: 5 Comprehensive Aspects.”

Automated Machine Learning in Microsoft Azure Data Science

Typically a real-life scenario like mentioned in the last section could take days or even weeks of experimentation. 

You would have to take the data received and experiment with it with different Algorithms and Hyperparameters to train the model and then repeat the process a bunch of times because you would need to guess and check the results in every iteration.

This is where Automated Machine Learning in Microsoft Azure Data Science comes in handy.

Automated Machine Learning generates different experiment runs using a combination of different Algorithms and Hyperparameters and then trains the models in parallel. It returns a Quality Score for each model after each run. 

Then, based on what it learns, it will generate different experimental runs with varying combinations of Algorithms and Hyperparameters to train better models.

Click here for more information on Automated Data Learning in Microsoft Azure Data Science.

Important Microsoft Azure Data Science Tools and Services

Microsoft Azure Data Science provides a spectrum of data science tools and services essential for a Microsoft Azure Data Scientist. 

You can use these tools to make Data Science projects efficient and scalable.

Listed below are some of the crucial tools and services of the Microsoft Azure Data Science platform:

• Azure Virtual Machine
• HDInsight Spark Cluster
• Azure Data Lake
• Azure Databricks
• Azure Synapse Analytics

1) Azure Virtual Machine

Azure Virtual Machine is one of the wide range of services that the Microsoft Azure Data Science platform offers to create your instances. A Virtual Machine is a computer file generally known as an Image that behaves like an actual computer. 

It runs in the window, giving you the same experience on a Virtual Machine as you would have on the host Operating System.

You can operate many Virtual Machines on the same physical machine using the Azure Virtual Machine service.

Each Virtual Machine has its hardware, such as a CPU and memory. It also has a lot of flexibility and maintains the physical hardware.

2) HDInsight Spark Cluster

The HDInsight Spark Cluster is an Apache Hadoop operating on the Microsoft Azure Data Science platform. Clusters are created using Hortonworks Data Platform (HDP).

HDP consists of Hadoop Core, the Hadoop Distributed File System (HDFS), MapReduce, HBase, Hive, Pig, HCatalog, etc.

HDInsight Spark Cluster configures the Clusters using multiple Azure Virtual Machines and can run on either Windows or Linux.

3) Azure Data Lake

Azure Data Lake is a Big Data Solution by Microsoft Azure Data Science. It gives you the ability to handle large volumes of data. Compared to SQL Databases, Azure Data Lake can handle larger volumes of data more quickly and efficiently. It can also manage Unstructured data. 

Azure Data Lake consists of 2 different services:

• Azure Data Lake Store: Azure Data Lake Store is where the data resides. It is a fully HDFS-compliant file system and runs on its own. It can integrate with Azure Active Directory, which helps you secure your data within Azure Data Lake.
• Azure Data Lake Analytics: Azure Data Lake Analytics simplifies Big Data. It uses the processed data to create reports and views.

“Read the Guide on the Best Data Ingestion Methods for Data Lakes to learn how to ingest your data in Azure Data Lake.”

4) Azure Databricks

Azure Databricks is an Apache Spark-based analytical service by the Microsoft Azure Data Science platform. It provides a one-click setup, streamlined workflows, and an interactive workspace that enables collaboration between Data Scientists, Data Engineers, and Business Analysts. 

Azure Databricks is based on Apache Spark. It has Spark SQL and Dataframes, a library that allows you to work on your Structured data. It also has Machine Learning libraries that enable you to prepare and train Machine Learning Models.

“Check out What is Databricks: The Best Guide for Beginners to achieve the full potential of data, ETL processes, & Machine Learning in Azure Databricks.”

5) Azure Synapse Analytics

Azure Synapse Analytics is the Microsoft Azure Data Science platform’s limitless Analytical tool. It brings Enterprise Data Warehousing and Big Data Processing into a single managed environment with no system integration required. 

Azure Synapse Analytics has Azure Synapse Link, a Cloud-Native Hybrid Transactional/Analytical Processing (HTAP) solution. It enables continuous analytics that does not interfere with your Operational or Application workloads. Hence, maintaining the performance of your application.

“Learn how to Set Up Azure SQL Analytics: A Comprehensive Tutorial.”

Frequently Asked Questions (FAQs)

What are the Best Azure Data Science Tools?

Some of the best Microsoft Azure Data Science Tools are Azure Virtual Machine, HDInsight Spark Cluster, Azure Data Lake, Azure Databricks, and Azure Synapse Analytics.

Which is better – Azure Data Factory or Azure Databricks?

Azure Data Factory (ADF) and Databricks are Cloud Azure Data Science Tools that use Extract-Transform-Load (ETL) and Data Integration techniques to manage extensive unorganized data and provide a firm foundation for analysis. 

Databricks streamlines Data Architecture by integrating Data, Analytics, & AI workloads on a single platform. In contrast, ADF is used for Data Integration Services to monitor data movements from diverse sources at scale.

You can read Azure Data Factory vs Databricks for a quick comparison between the two.

What is the difference between Azure Databricks and Azure Synapse?

Azure Synapse combines business data warehousing & big data analytics into a single platform. In contrast, Databricks does big data analytics and allows customers to create advanced machine learning models at scale. 

Refer to Azure Synapse vs Databricks: 6 Critical Differences to learn more.

What are the Benefits of Azure Data Lake?

Azure Data Lake is one of the robust Data Science Tools and cloud storage solutions that can ingest, store, & analyze data while seamlessly connecting with your Data Stores & Data Warehouses. It combines low-cost, tiered storage, high availability, & disaster recovery capabilities.

How can I Become a Certified Microsoft Azure Data Scientist?

The Microsoft Azure Data Scientist Associate requires you to know the ML, AI, NLP, computer vision, and predictive analytics concepts and a hands-on on Azure Data Science Tools. 

Read more about the Microsoft Azure Data Scientist Certification.

What is Azure Data Science Virtual Machine?

The Azure Data Science Virtual Machine (DSVM) is a VM image for data science on the Azure cloud platform. Many popular data science tools are pre-installed and configured to help you build intelligent advanced analytics applications.

Conclusion

Microsoft Azure Data Science platform proves to be an added advantage for your business. With the above understanding of Data Science Tools and Microsoft Azure Data Science services, you can create detailed reports and generate better insights from your data. 

Your work as a Microsoft Azure Data Scientist or Microsoft Data Analyst involves regular data transfers for analytical purposes. 

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Also please do share your experience of this blog in the comments section below and do ask us as many questions as you have. We will surely respond 😊