With companies generating more data than ever, Data Science has become an important part of their Business Model. Companies are using Data Science Predictive Models to extract business-focused insights from their data. This helps them to take strategic decisions and boost their business. Working with data and creating Data Science Models could be a tedious and complex task. This is where the Microsoft Azure Data Science platform comes to your aid.
Microsoft Azure Data Science provides you an environment to prepare and train your Data Science Models. As of now, it provides you 22 different categories of Cloud Services that are useful for the Data Science Model Life Cycle including Artificial Intelligence (AI) and Machine Learning (ML), Blockchain, Networking, Containers, Analytics, Storage, Security, Databases, Compute, etc.
This article provides you an overview of Microsoft Azure Data Science and how it is significant 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
Introduction to Data Science
Data Science, in simple terms, is the field where Computer Science meets Statistics. In Data Science, we use scientific methods to turn data into values by asking the right questions, creating hypotheses, and devising experiments to test these hypotheses. These experiments eventually result in conclusions, discoveries, and inventions. In the case of Artificial Intelligence and Machine Learning, these result in Predictive and Prescriptive Models, which help you extract meaningful insights from your data and make strategic decisions for your business.
There are 4 Pillars of Data Science. In an ideal world, these Pillars represents the areas that Data Scientists should be expert on. These Pillars are Business/Domain Expertise, Mathematics (including Statistics and Probability), Computer Science (including Data Architecture and Engineering), and Communication (both written and verbal).
For more information on Data Science, click here.
Introduction to Microsoft Azure Data Science
Microsoft Azure Data Science provides a huge number of tools and services to Microsoft Data Analysts or Microsoft Azure Data Scientists for easy analysis and developing Predictive Models. In the past, 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, for a single Predictive Model, you needed to integrate storage tools like Azure Blog Storage or Azure Data Lakes Storage as you cannot train your models without data. Virtual Machines, Spark Cluster, Azure HDInsight, or Azure Databricks to run your code and train your model. Then to manage and secure your data for enterprise readiness you needed Virtual Networks and Azure Key Vault.
Moreover, if you wanted to repeat your experiments using a consistent set of Data Science libraries and the different versions, then you needed Docker Containers and Azure Container Registry to store those Docker Containers. You then needed to put everything inside your Azure Virtual Network (VNet). To run all this on a scale you also needed Azure Kubernetes Service inside your VNet.
Doesn’t this sound like a very complicated 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 for you. 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 as well as 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 in accordance with the data that you have collected. This is to find meaningful insights from the data.
- Training Models: This involves training or refining the Predictive Model that 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.
For more information on Microsoft Azure Data Science, click here.
Simplify Data Analysis Using Hevo’s No-code Data Pipeline
Hevo Data is a No-code Data Pipeline that offers a fully managed solution to set up data integration from 100+ data sources (including 30+ free data sources) to numerous Business Intelligence tools, Data Warehouses, or a destination of choice. It will automate your data flow in minutes without writing any line of code. Its fault-tolerant architecture makes sure that your data is secure and consistent. Hevo provides you with a truly efficient and fully automated solution to manage data in real-time and always have analysis-ready data.
Let’s look at Some Salient Features of Hevo:
- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects schema of incoming data and maps it to the destination schema.
- Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
- Hevo Is Built to Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
- Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
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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 works and the 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 discreet pipeline for each model. This also helps them to incorporate reproducibility in the entire Data Science Model Life Cycle including training, testing, and production environment. Hence, makes it easier for them to track and reproduce their model.
For example, assume that you have a wind farm that you manage and you want to both optimize the energy output and have a predictive maintenance scenario to avoid any downtime. So, each of your discreet pipelines helps you consistently build, train, package, and deploy Data Science Models to different windmills and iterate as new data comes in. This helps to train and deploy new Data Science Model versions to ensure that you have the best quality models in production.
Automated Machine Learning in Microsoft Azure Data Science
Typically a real-life scenario like mentioned in the last section could take days or maybe even weeks of experimentation. You would not only have to take the data received and experiment with it with different Algorithms and Hyperparameters to train the model but 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 keep generating different experimental runs with different combinations of Algorithms and Hyperparameters to train better models.
For more information on Automated Data Learning in Microsoft Azure Data Science, click here.
Important Microsoft Azure Data Science Tools and Services
Microsoft Azure Data Science provides a spectrum of tools and services that are very important for a Microsoft Azure Data Scientist. These tools are used to make the Data Science projects efficient and scalable. Listed below are some of the important tools and services of the Microsoft Azure Data Science platform:
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.
With Azure Virtual Machine service, you can run multiple Virtual Machines simultaneously on the same physical computer. Each Virtual Machine provides its own hardware like CPU, Memory, etc. It also offers a high range of flexibility and maintains the physical hardware that runs on it.
For more information on Azure Virtual Machine, click here.
2) HDInsight Spark Cluster
HDInsight Spark Cluster, in simple terms, can be described as Apache Hadoop running on the Microsoft Azure Data Science platform. It uses Hortonworks Data Platform (HDP) configurations for creating Clusters. HDP is a complete set of the most important components of the Hadoop Ecosystem. It consists of Hadoop Core which is the Hadoop Distributed File System (HDFS) and MapReduce, HBase, Hive, Pig, HCatalog, etc. HDInsight Spark Cluster configures the Clusters using multiple Azure Virtual Machines and can be run on either Windows or Linux.
For more information on HDInsight Spark Cluster, click here.
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 is able to handle larger volumes of data more easily and efficiently. It can also be used to handle 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 complaint file system and runs on its own. It is also integrated with Azure Active Directory which helps you to secure your data within Azure Data Lake.
- Azure Data Lake Analytics: Azure Data Lake Analytics simplifies Big Data and is where the data is processed and transformed to create reports and views.
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. As it is based on Apache Sparks, it has Spark SQL and Dataframes which is a library that allows you to work on your Structured data. It also has Machine Learning libraries that allow you to prepare and train Machine Learning Models.
For more information on Azure Databricks, click here.
5) Azure Synapse Analytics
Azure Synapse Analytics is the Microsoft Azure Data Science platform’s limitless Analytical tool. It brings together Enterprise Data Warehousing and Big Data Processing into a single managed environment with no system integration required. It has Azure Synapse Link which is a Cloud-Native Hybrid Transactional/Analytical Processing (HTAP) solution. It enables continuous analytics over Operational Data in Azure Cosmos DB. This continuous analytics does not interfere with your Operational or Application workloads. Hence, maintaining the performance of your application.
For more information on Azure Synapse Analytics, click here.
This article introduced you to the Microsoft Azure Data Science platform. It also proved the effectiveness of using the Microsoft Azure Data Science platform and its services for your business. It also briefed you on the tools and services of the Microsoft Azure Data Science platform that every Microsoft Data Analyst or Microsoft Azure Data Scientist should be aware of.
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