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Benefits Of Data Science In AWS

This article was published as a part of the Data Science Blogathon.

 

Data Science In AWS
Source: Sigmund on Unsplash

In this article, we will discuss the advantages of building knowledge in data science that comes within the cloud. We begin by discussing the advantages of cloud computing. 

Next, we tend to describe a typical machine learning workflow and also the common challenges to maneuver our models and applications from the prototyping section to production. We touch on the overall advantages of developing data science applications on Amazon Web Services (AWS) and introduce the relevant AWS services for each step of the model development workflow. We will also look additionally share the field of study best practices, specifically around operational excellence, security, dependableness, performance, and price improvement.

 

Benefits of Cloud Computing

Cloud computing permits the on-demand delivery of IT resources via the web with pay-as-you-go pricing. Thus rather than shopping, owning, and maintaining our data centers and servers, we will acquire technology like compute power, storage, databases, And other alternative services on an as-needed basis. Just like a power company sending electricity instantly when we flip a light switch in our home, the cloud provisions IT resources permits with the click of a button or invocation of an API.

“There is no compression algorithm for experience” is a famous quote CEO of AWS. The quote expresses the company’s long-standing expertise in building reliable, secure, and performant services since 2006.

AWS has been regularly increasing its service portfolio to support nearly any cloud work, as well as several services and options within the space of computer science and machine learning. Several of those AI and machine learning services stem from Amazon’s pioneering add recommender systems, computer vision, speech/text, and neural networks over the past twenty years. A paper from 2003 titled “Amazon.com Recommendations: Item-to-Item Collaborative Filtering” recently won the IEEE award as a paper that withstood the “test of your time.” Let’s review the advantages of cloud computing within the context of data science with AWS.

 

Agility

 

Data Science In AWS agility
 Source: Skye Studios on Unsplash

Cloud computing lets us spin up resources as we want them. This allows us to experiment quickly and whenever we want. Perhaps we would like to check a new library to run data-quality checks on our dataset or speed up model training by leveraging the latest generation of GPU compute resources. We would spin up tens, hundreds, or maybe thousands of servers in minutes to perform those tasks. If an experiment fails, we will continually de-provision those resources with no risk.

 

Cost Savings

Cloud computing permits us to trade capital expenses for variable expenses. We tend to solely pay money for what we use with no upfront for direct investments in hardware which will become obsolete in a very few months. If we tend to spin up compute resources to perform our data-quality checks, date transformations, or model training, we only solely pay money for the time those compute resources are in use.

We will deliver the goods any value savings by investing Amazon EC2 Spot Instances for our model training. Spot Instances allow us to profit from unused EC2 capability within the AWS cloud and are available with up to a 90% discount compared to on-demand instances. Reserved Instances and Savings Plans permit us to avoid wasting money by prepaying for a given quantity of your time.

 

Elasticity

 

Elasticity Data Science in AWS
Source: aimconsulting

Cloud computing permits us to scale our resources up or down to match our application needs. Let’s say we’ve deployed our data science application to production and our model is serving real-time predictions. we will currently automatically rescale the model hosting resources just in case we tend to observe a peak in model requests. Similarly, we will certainly scale down the resources once the quantity of model requests drops. there’s no need for overprovision resources to handle peak loads.

 

Innovate quicker

 

Innovate quicker
Source: Source on Source

Cloud computing permits us to innovate quicker as we will specialize in developing applications that differentiate our business, instead of defrayal time on the uniform work of managing infrastructure. The cloud helps us to experiment with new algorithms, frameworks, and hardware.

 

Deploy Globally in Minutes

 

Deploy Globally in Minutes
Source: iamwire

Cloud computing lets us deploy our data science applications globally within few minutes. In our world economy, it’s vital to be on the point of our customers. AWS has the idea of a Region, that may be a physical location around the world where AWS clusters data centers. Every group of data centers is called an Availability Zone (AZ). Every AWS Region consists of multiple, isolated, and physically separate AZs inside a geographical area. The quantity of AWS Regions and AZs is endlessly growing.

We can leverage the global footprint of AWS Regions and AZs to deploy our data science applications on the point of our customers, improve application performance with fast response times, and comply with the data-privacy restrictions of every Region.]

 

Smooth Transition from epitome to Production

Smooth Transition from epitome to Production
Source 

One of the advantages of developing data science applications that comes within the cloud is that the sleek transition from epitome to production. We can switch from running model code to data-quality checks or distributed model training across petabytes of data in less time. And once we are done, we will deploy our trained models to serve a period or batch predictions for users across the world.

Prototyping usually happens in single-machine development environments victimization Jupyter Notebook, Numpy, and pandas. This approach works fine for not large datasets. While on work with large datasets, we’ll quickly exceed the only machine’s hardware and RAM resources.

Also, we tend to might want to use GPU or multiple machines to accelerate our model training. This is often sometimes impracticable with one machine. The next challenge arises once we wish to deploy our model (or application) to production. we also need to keep in mind that this application will handle thousands of users on a world scale.

Production preparation usually needs a robust collaboration between numerous groups as well as data science, data engineering, application development, and DevOps. And once our application is successfully deployed, we want to endlessly monitor and react to model performance and data-quality problems that will arise when the model is pushed to production.

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