When data scientists are looking forward to selecting a cloud platform for his next big project, how do they select the one considering the number of options currently present in the market. More importantly, what are the general features due to which a data scientist uses a cloud computing service and how much does a data scientist require to shell out of his or her wallet to acquire the services.
In this article, we shall jump into the shoes of a data scientist and try to understand the cheapest cloud computing service one can zero in to complete the necessary task. In order to do that, a comparison must be put forward in terms of the number of available services or features, the number of free services and the cost of some services to reach a clear and crystal conclusion. Kindly note that this article takes into account the usage made by a data scientist on the personal front and not on the professional side for any organisation.
Usage of Cloud Computing
A data scientist can use a cloud computing service for a host of different services, which one cannot simply access on a desktop because a personal computer cannot support high-computing for training neural networks with a plethora of data. It is not possible to create a development environment that can be capable of taking in large datasets or keep training models continuously. Moreover, cloud computing is cost-effective and flexible in nature, which allows a great deal of scalability and reliability that is hard to expect from local machines installed at home. Not to mention, the improved operational efficiency is an overall bonus for any data scientist who is on the clock.
Over the years, cloud computing has made the job extremely easy for a data scientist with its mind-boggling features and among many. Serverless computing is another service, which data scientists rely on very often since it lets them run codes without the need for managing any servers. Let’s not forget the pricing factor, which is closer to not more than one grand rather than affording a high-end system that is bound to lag at some point in time.
With a wide array of services, it is quite understandable now why a data scientist relies more on cloud computing services. However, which one stands as the cheapest is yet to be figured out so let’s take a look at some of the cloud computing services.
Amazon Web Services (AWS)
Since computing is the first service that a data scientist is likely to access to begin any project, AWS comes with a number of important ones such as EC2, Lambda and Batch. The EC2 provides a secure compute capacity in the cloud, which is freely available for 750 hours of Linux and RHEL per month for a period of 12 months. Besides, a number of storage services are provided as free by the tech giant such as Amazon S3 and Amazon CloudFront with 5GB and 50GB of standard storage for a period of 12 months.
Moving on to databases, Amazon Relational Database Service (Amazon RDS) allows to easily operate databases in the cloud with several familiar engines such as Amazon Aurora and Oracle Database to name a few, with 750 hours of free usage within a period of 12 months. A data scientist can also access a number of machine learning tools such as the SageMaker for a free trial of 250 hours and can request 10,000 texts per month from Amazon Lex to build voice and chat text chatbots.
AWS also provides certain free access to analytics and robotics to ease the job of a data scientist. In terms of analytics, a data scientist can use Amazon Elasticsearch service for 750 hours. Last but not least, a data scientist can also use robotic service such as the RoboMaker, which makes it easy to develop, simulate, and deploy intelligent robotics applications for a free period of 25 SU hours. In the recently held AWS re:Invent, a number of other machine learning services were unveiled such as Augmented AI CodeGuru to name a few.
Google Cloud Platform
From creating applications to securely managing data and getting insights from data faster, Google Cloud Platform or CGP as widely known by everyone, has provided services and tools for years to data scientists and has made their job easy. The tech giant allows a free credit of $300 to new customers for the first 12 months along with access to several free products such as BigQuery and Compute Engine, with monthly limits only.
The tech giant provides one free F1-micro instance per month as compute engine and two million cloud run requests per month. Storage capacity of 5GB is provided every month absolutely free of cost to all users.
Moving on to AI and ML, GCP provides 60 minutes of free speech-to-text transcription per month, 5000 units of natural language processing and Vision AI for 1000 units are available for a month. Moving on to data analytics, CGP comes with 10GB of messages ingestion and delivery per month for Publish-subscribe pattern.
Another tech giant with a revolutionary platform, Azure lets a user create a free account to start with. The company has put enough transparency on its website and has very clearly listed the products that are free for a certain time, the paid ones and the ones, which will be kept free forever as stated by the company. Once an account is created, a user will be provided with ₹13,300 credit and can explore any Azure service absolutely free for a period of 30 days. Along with this, a user gets 12 months of selected free services and more than 25 lifetime free services. In the first month, a user can test and deploy enterprise apps to gain insight from data.
Moving on to other free services for 12 months, a user has access to 750 hours of Linux and Windows Virtual Machine usage. 5GB of Blob and File Storage along with 250GB of SQL database is also among the free services. In the list of always free services, a user can quickly create 10 web, mobile or API apps and a free machine learning server to name a few.
Three tech giants have ruled the scenario for long enough to be not judged, but where there is competition, there has to be a judgement. In terms of cheapest services, Amazon Web Services can be ruled out as the cheapest one in terms of the number of free services available for the first 12 months. Although when compared to Microsoft Azure, the always free product list falls short with 22 products on the list compared to Azure’s more than 25 free services. The comparison becomes slightly tough with Azure’s ₹13,300 credit, but the usage of a more visual interface may not be preferred by everyone as it provides a lesser insight. Indeed, AWS costs money once the free period is over, but it always comes with a full suite. In case of confusion for which one to opt, a reader can read our article ‘Which Cloud Platform To Embrace For AI Workloads’ to find the suitable need.