The number of gadgets that are interconnected via the internet-of-things is appreciating at a pretty fast rate. Statista forecasts that there will be approximately 50 million IoT-connected devices in deployment globally by the end of this decade. And these connected gadgets and enterprise systems will produce massive amounts of data. And a majority of this data will be recorded and undergo analysis on the cloud.  

The cloud provides access to various computing services such as databases, servers, software, data analytics, and artificial intelligence, amongst others. It enables enterprises to execute their applications and record information on the best datacentres with reasonable expenditure. This enables them to simplify and quicken their data science efforts. And as data storage and analysis are being prioritized by several enterprises, brining cloud computing strategies and data science together can help generate more revenue. 

Data Science is empowered through cloud computing 

Conventionally, organizations recorded their data on localized servers prior to the proliferation of cloud computing. Data scientists and engineers had to go about transferring data from the centralized servers to their systems each time they wished to execute data analysis. The procedure was incredibly complex and time-intensive as data analysis necessitates gathering and segregating incredible volumes of data. Further, developing and administration of servers within the premises can be very costly. They need ongoing maintenance and backups to avert data loss. Organizations can also wind up having too many or too little servers to fulfil their data requirements. 

This is where cloud computing bailed organizations from the complications of physical servers. 

Cloud computing has led to the democratization of data. Both small and large enterprises can execute data analytics without the expenditure related with servers and storage. It has also made data management simpler, and also data analytics for data scientists. Cloud computing facilitates data scientists to leverage the easy to access data and concentrate on data analysis, evaluating hypothesis, and producing robust machine learning (ML) functionalities. 

Generating value with the cloud 

A report predicts that the international cloud computing market size will get to $832.1 billion in four years from now, from $371.4 billion just a year back. It is therefore no shock that cloud data centres are poised to process 94% of workloads by this year. And as cloud computing and data science are basically interconnected, there are several benefits of taking up cloud for ML and data science projects. The following are the five leading advantages: 

Savings in expenditure: Several cloud computing services feature a pay-per-use model. This eradicates the requirement to pay for data recording space or features that enterprises do not require or want. For instance, when an organization undergoes an increase/decrease in its data science or ML loads, it can easily scale up or down it cloud server utilization and make payment accordingly. However, if an organization wishes to scale its on-premise server, it will have to obtain costly hardware. Therefore, leveraging cloud computing can have the outcome of considerable cost savings. 

Real-time data administration: By recording data in the cloud, organizations can eradicate any delays in the data flow. The cloud functions as a centralized and accessible platform that facilitates data scientists to easily handle multi-structured data in real time. 

Quicker collaboration: Cloud computing facilitates swifter collaboration. Data scientists and engineers alike can easily look at, share, and process information throughout a cloud-based platform. By leveraging cloud collaboration, they can give input and real-time updates from anyplace, at any moment. 

Preventing data loss: Some enterprises record all of their data on localized servers/hardware. In the scenario these localized servers/hardware fail, these enterprises might wind up losing their critical corporate data for good. However, with cloud servers, all of the information gets safely recorded on the cloud. This data can be easy to access from any smart gadget which is connected to the internet. 

Improved information security: RapidScale estimates that 57% of enterprises hold the belief that cloud imparts improved information security when contrasted with legacy systems. As a matter of fact, over half of enterprises record confidential and sensitive information within the cloud. The data communicated over networks and recorded on the cloud undergoes encryption. This encryption puts the data out of reach of hackers. 

Cloud computing platforms for Data Science 

Based on Kaggle’s 2020 ML and Data Science Survey, 83% of data scientists who took the survey are leveraging the cloud. The most prominent cloud computing figures consist of Amazon Web Services, Microsoft Azure, and Google Cloud Platform. Other players in the game are IBM Cloud, VMWare Cloud, Oracle Cloud, and Salesforce Cloud. The following are the profiles of these forerunners. 

Amazon Web Services 

First making it to the market all the way back, 15 years ago, in 2006, Amazon Web Services is at the present the hottest cloud computing platform in the marketplace. Information from Synergy Research Group demonstrates that Amazon Web Services market share in the international cloud infrastructure market was 32%, in the final quarter of 2020. The platform consists of several products for databases, which includes Amazon DynamoDB and Amazon Aurora. It also carries products concerned with data analytics, which includes AWS Data Pipeline, Amazon RedShift, Amazon QuickSight, and Amazon EMR. Amazon Web Services has exhaustive security functionalities and robust controls. 

Google Cloud Platform 

Launched 13 years ago, way back in 2008, Google’s cloud platform furnishes cloud computing services that function on the same infrastructure that Google leverages for its products like Google Search, YouTube, and Gmail. It features various products concerned with data analytics, which includes Dataproc, BigQuery, Google Data Studio, and Dataflow. Google Cloud Platform can assist scientists to easily develop, evaluate, and deploy ML models and collaborate with regards to their enhancement. 

Microsoft Azure 

11 years ago, in 2010, Azure was launched as a cloud computing platform concerned with data analytics and data science. It provides compatibility for databases via its products which includes Azure Cosmos DB and Azure SQL Database. It has features products concerned with data analytics, which includes Azure Data Factory, Azure Synapse Analytics, Azure Stream Analytics, and Azure Data Lake Storage. This platform makes sure that engineers and data scientists alike can avail of simple predictive data mining. Going by the previously mentioned Synergy Research Group data, Microsoft Azure had capture 1/5th of the international cloud infrastructure market in the fourth quarter of 2020. 

As enterprises continue to accelerate their digital transformation efforts to retain their competitive edge, it is also critical to empower their data science functionalities with cloud computing. Data science is not only about the process of data. It needs a strong infrastructure to take in data and for data scientists to develop predictive models on the basis of insights. Combining cloud computing to this framework can work wonders. It can considerably streamline the data science processes and assist an enterprise to transform and accomplish its objectives.