The advancement of technology in recent times has allowed companies to reap the benefits of streamlined processes and cost-efficient operations. But the one thing that has become a game-changer for businesses of all sizes is the availability of data from every source one can think of.
A well-known fact about data being produced on an everyday basis is as follows: Internet users generate roughly about 2.5 quintillion bytes of data daily.
1 quintillion bytes = 10^9GB
These large stores of data that are bombarded to companies’ day in and day out are collectively known as big data. Roughly, it is estimated that out of the total data that is being generated only 12% of data is being processed and analyzed.
The main reason behind this is data storage and analysis are the two utmost challenges for businesses, whether small, mid, or large. Recording information in a cost-efficient manner and securely is one of the top priorities of businesses.
That’s where Cloud Computing comes to the rescue.
This blog covers Why a Data Scientist should know Cloud Computing tools and technologies? How it can be leveraged by Data Scientists to make model deployment faster and flexible to scale the model?
What exactly the cloud is?
A cloud describes the situation wherein a single party owns, administers, and manages a group of networked computers and shared resources typically to host and provide software-based solutions.
What’s the problem of Data Scientists today?
Once the development environment is set up and good to go, the typical data science workflow or an Iterative process begins which includes:
- Acquiring data
- Parsing, munging, wrangling, transforming data
- Analyzing and mining the data, such as EDA, summary statistics.
- Building, validating, and testing models.
- If not building models, then identifying patterns or trends, generating actionable insights, extracting useful information, creating reports. And finally, tuning and optimizing models or deliverables
Sometimes, it is not practical to perform all data science-related tasks in one’s local development environment. Here are some of the main reasons why:
- Too large datasets will not fit into the development environment’s system memory (RAM)
- The local CPU might not be able to perform tasks in a reasonable or sufficient amount of time,
- The deliverable is required to be deployed to a production environment and possibly incorporated as a component into a larger application.
- Why not use servers? The most obvious reason is that a server needs space to be stored.
- Server infrastructure becomes expensive to buy and set up.
- In-house data storing requires to have backups and ideally have them in different locations.
- For fast-growing companies, server needs could become most unpredictable. With in-house servers, one usually ends up buying more servers than actually needed at a given time.
Top three Cloud Computing tools
Over these years, cloud computing has made the job extremely easy for a data scientist with its mind-boggling features. Serverless computing is another service that data scientists often rely on since it lets them run codes without managing any servers. With a wide array of services, it is quite understandable now why a data scientist will continue to rely more on cloud computing services. Let’s take a look at some of the leading cloud computing services in the market.
The three Major Cloud Computing tools are:
- AWS(Amazon Web Services)
- Microsoft Azure
- GCP(Google Cloud Platform)
Amazon Web Services
AWS comes with several important ones such as EC2, Lambda, and Batch. The EC2 provides:
- A secure compute capacity within the cloud, freely available for 750 hours of Linux and RHEL/month for a period of 12 months.
- Several 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 data scientists to easily operate databases in the cloud with several familiar engines such as Amazon Aurora and Oracle Database, with 750 hours of free usage within a period of 12 months.
A data scientist can also access machine learning tools such as the SageMaker for a free trial of 250 hours and can request 10,000 texts/month from Amazon Lex to build voice bots and chatbots.
Another tech giant with its revolutionary platform, Azure lets a user create a free account to start with. Once an account is created, a user will be provided with a credit amount of about ₹13,300 and can explore any Azure service absolutely free for a period of 30 days. Along with this, a user gets a year of selected free services and more than 25 lifetime free services
Moving on to free services, the user has access to 750 hours of Linux and Windows VM usage,5GB of Blob and File Storage along 250GB of SQL database among the free services. A user can create 10 webs, mobile or API apps, and a free machine learning server in the list of always free services.
Google Cloud Platform
From creating applications to securely managing data and getting insights from data faster, Google Cloud Platform has provided services and tools for years to data scientists and has made their job easy. It allows a free credit of $300 to new customers for the first 12 months, with access to products like BigQuery and Compute Engine. GCP provides 1 free F1-micro instance/month as a compute engine and 2 million cloud run requests/month and 5GB/month storage capacity absolutely free of cost.
Which Cloud to choose?
In terms of cheapest services, AWS can be ruled out as the cheapest in terms of the number of free services available for the first 12 months. In comparison 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 ₹13k credit, but the usage of a more visual interface may not be preferred by everyone. AWS costs money once the free period gets over, but it comes with a full suite