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Azure for Data Science: Overview, Challenges, Technologies
Published
28th Dec, 2023
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Cloud computing, along with data science has been the buzzword for quite some time now. Companies have moved towards cloud architecture for their data storage and computing needs. The reasons are on-demand availability of the computer resources, less or no active management by the user, quick or on-the-fly setup, customizable as per the needs, etc. Microsoft Azure is one such public cloud computing platform that provides a range of cloud services for computing, storing, and networking.
There are some renowned cloud players like Amazon Web Services, Google Cloud, IBM Watson, etc., but Microsoft’s Azure platform is believed to cater to around 95% of Fortune 500 companies, including Samsung, HP, Walmart, Verizon, and Pixar. It is one of the fastest-growing cloud platforms with its servers present in more than 60 regions and services being used in more than 140 countries.
So, it surely makes sense to learn and develop expertise in Azure skills. This article illustrates the blend between a data scientist and Azure as a cloud platform. You can learn more about Data Science course that helps you understand how to tackle complex data science problems through practical exercises. This will help you in your journey to become an Azure Certified Data Scientist or Microsoft’s DP 100 exam preparation.
Azure for Data Science: Overview
Azure, a cloud computing service created by Microsoft for building, testing, deploying, and managing applications and services through Microsoft-managed service centres is an excellent choice for your data science needs. A Data Science Azure Certification is required from Microsoft to call yourself a Microsoft-certified Azure Data Scientist. An Azure Data Scientist specializes in extracting valuable insights from large data sets. They apply data analysis, machine learning, and statistical techniques to interpret complex data and make informed decisions. I use Azure tools and services for my data science applications and machine learning experiments. It helps me to perform data analysis and visualizations with just a few clicks and train predictive models, design and develop data-driven solutions for my organization.
Why Use Azure for Data Science?
Data Science is heavily reliant on computing resources. Building Machine Learning (ML) or Artificial Intelligence (AI) models, requires work on big data. The computing needs to manage the big data is costly if we decide to set up an on-premises server and computing capabilities. Also, it is difficult and costly to scale and manage them. Therefore, to take care of the data science applications, cloud computing is a feasible choice.
With Azure, we need not worry about procuring the actual hardware and packing it or plugging it into the power supply or any other maintenance activity. This hardware is present at some dedicated locations around the world and managed by the Microsoft Azure team. An Azure data science course can demonstrate the Azure services in detail which will help you as a data scientist to save time and effort and achieve better results.
Skills Required for Azure Data Scientist
As a Data Scientist, there are certain sets of skills one should have to make the best use of the Azure platform for your data science tasks. Some of these skills are a part of your data science expertise and the remaining as part of cloud proficiency.
1. Data Pre-processing
Data pre-processing is the preliminary step towards any data science application. We are required to pre-process the raw data through steps such as data cleaning, data transformation, and feature engineering. Pre-processed data can then be utilised to perform data visualisations and training models for analysis.
2. Data Visualisation and Analysis
Microsoft Azure provides an interactive data visualization tool known as Power BI. It helps to visualize any data and build intuitive dashboards to present to stakeholders or colleagues. This helps in the analysis of the data on several points and makes informed decisions. We can leverage this tool from the Azure platform if we have an understanding of data visualisations and how to interpret and analyse them. Power BI is easy to use and saves a lot of time during the data visualization and analysis step.
3. Programming Language
While Azure has support for almost all programming languages, it is strongly advised one have an intermediate level knowledge about Python or R programming language. Python is the most widely used programming language for data science tasks followed by R. They have a vast list of libraries which aid in data science tasks. These skills enable Azure Data Scientists to create custom solutions, automate processes, and present findings effectively.
4. Machine Learning Algorithms
A good understanding of machine learning algorithms is a must for every data scientist. The model-building step requires you to have a thorough knowledge of these algorithms, especially how they work and when to use them. Azure provides a Low-Code No-Code (LCNC) application development through a variety of service offerings. The Azure AIML service can help us select the right algorithms, train and evaluate them, and fine-tune the models to provide accurate predictions.
5. Cloud Computing Services
To make sure you utilise the Azure cloud platform to its full potential, one must have a basic understanding of cloud computing and knowledge of the services offered by the Azure cloud platform.
I highly recommend you go through the Azure Data Scientist Associate Certification which can help you to understand the different services and offerings of Azure cloud computing. You can check one of the best Data Science Bootcamp trainings that can aid you in your journey to becoming an Azure Data Scientist.
Challenges of Azure for Data Science
While there are plenty of advantages for data scientists designing and implementing a data science solution on Azure, there are some challenges that come along with it. To start with, the foremost challenge is the data sensitivity. For an organisation where data sensitivity cannot be compromised at any cost, it is a challenge to use Azure services or any cloud services for that matter.
- Although the cloud providers ensure for highest level of safety for the data, it still faces a potential risk. As per the reports, Microsoft AI Research division accidentally leaked 38 terabytes of private data via unsecured cloud storage. Although there are a lot of ML models on the Azure Machine Learning services it still lacks some new additions or is limited to one or only a few programming languages.
- Another challenge while using Azure can be the transition to a different cloud service. I used Tableau, a collaborative data visualization and analysis software, for a long time and had good hands-on over it. But moving to Microsoft Azure, I had to switch to Power BI for my data visualization requirements which was a challenge as I was used to a platform already.
- A good addition will be the incorporation of some widely used independent services on the Azure platform. One must also keep a note that working with cloud services requires an always active and healthy internet connection which might be a problem for people, teams, or organisations operating from remote areas. Despite these challenges, Azure still serves as a beneficial platform for data scientists.
Technologies of Azure Data Science
In this section, we will look at some of the tools and services provided by Azure for Data Science operations. These tools and services help us to collaborate with other data science professionals and address challenging business issues.
- Azure Compute provides the necessary infrastructure. If we need a virtual machine, virtual desktop, or web apps, we can get all that using the Azure Compute services. This can be clubbed with the Azure Networking resources which deal with the virtual network managing firewalls, server traffic, etc.
- Azure Storage is a cloud storage solution that enables us to store and access data in the cloud. It is highly scalable, efficient, and secure with options to store the data in five different formats, namely, file, disk (HDD or SSD), blob, table, and queue.
- Azure Machine Learning helps to build ML and AI services for end-to-end machine learning lifecycle that includes machine learning operations (MLOps). It empowers data scientists and developers to build, deploy, and manage high-quality models faster.
- Azure Data Factory aids in constructing an ETL (Extract, Transform, Load) pipeline without writing our code. It offers a fully managed, serverless data integration service that helps to automate the data pipeline.
- Azure Data Bricks offers an optimized big data analytics solution in an interactive workspace. It provides Apache Spark clusters to work with and has support for almost all the required programming languages, data science frameworks and libraries.
- Azure Synapse Analytics is a one-stop solution for all data analysis requirements. It is a powerful service provided by Azure that can accelerate data analytics workload across data warehouses and big data systems.
How Azure and Data Science are Shaping the Future?
By now, we know that Microsoft Azure is a cloud computing service, created by Microsoft for building, testing, deploying, and managing applications and services. The Azure services are decoupled, which means, we can create virtual machines with different combinations of storage, RAM and space.
It is a fully managed cloud-based service that is helping data scientists to design predictive analytics solutions. We have seen how organisations on the back of digital transformation sailed through the tough times during the pandemic in 2020.
Azure provides a digital ecosystem that has helped data scientists tackle constant challenges with data tools and technologies. A Microsoft Azure Data Scientist certification ensures that a candidate learns how to design and create data science workloads, run jobs, and manage, deploy, and monitor scalable machine learning solutions.
From long hours of exploratory data analysis, selecting machine learning algorithms, scaling hardware requirements, and data warehousing challenges to efficient, effective, and quick application development, Azure has helped organisations to break down all silos to achieve predictive power from the data.
The customers or partners of Azure services include organizations from healthcare, financial services and banking, manufacturing, retail, government, and gaming sectors. Azure data scientists through the help of Azure Analytics services have enhanced patient care by improving patient engagement, provider collaboration, and operations.
Financial companies with the help of Azure are optimizing risk and fraud management and transforming customer experience. Retail customers are benefitting from Azure’s capability to optimize supply chains, create tailored experiences, and use of latest technologies for enhancing customer experience. Azure has dedicated services for the latest technologies like the Internet of Things (IoT), Mixed Reality (AR VR), Blockchain, etc. which are shaping data science applications.
Conclusion
In this article, we have discussed how the Azure platform is becoming a significant skill to add to the life of a data scientist. In recent years, Microsoft has worked hard to bring new advanced features to its Azure platform. It has provided dedicated services to the latest technologies, updated existing services to include effortless usage, and continues to update the Azure platform for a much smoother experience.
Due to this, it makes sense to go through Microsoft’s exam DP 100 course and Data Science Azure certification course to know more about their latest offerings and the full potential of the platform for a data scientist. If you are a beginner or want to enhance your skills as a data scientist then check out this Knowledgehut Data Science course that covers data cleaning, mathematics, statistics, SQL, Python, Tableau, ML, DL, and more.
Frequently Asked Questions (FAQs)
1. Is Azure used for data science?
Being a Microsoft Azure Data Scientist Associate, I can assure you Azure is a powerful platform for building data science applications from scratch. To know better about the data science services being offered by the Microsoft Azure cloud platform, I highly recommend you go through the Microsoft Certified Azure Data Scientist Associate Certification.
2. Which is better AWS or Azure for data science?
Both AWS and Azure are excellent cloud-based platforms for data science. AWS is usually preferred for its lower pricing compared to Azure services while Azure being a Microsoft service adds up greatly with the Microsoft business offerings.
3. Is Azure data science worth it?
Yes, it is worth using Azure tools and services. It helps to save time and operational costs of setting up an ecosystem for data science operations. The Microsoft Azure DP 100 Exam illustrates how we can design and Implement a Data Science solution on Azure.
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