Getting Started with AWS Machine Learning Models for eCommerce

By Nick Epson on March 19, 2020

We’ve been hitting the AI topic a lot in recent times, as there is much to say on the matter, so let’s take a look at machine learning models for eCommerce in AWS. While onboard systems such as Apple’s CoreML are useful for local, unsupervised learning (and beyond) the most significant machine learning typically happens on backend systems like AWS with tools geared for eCommerce.

Mobile platforms and software everywhere are benefiting from intelligent systems, thanks to AI-driven backends running on systems like Microsoft Azure, Google Cloud’s AI Hub and AWS machine learning including their SageMaker product. Here, we’re going to focus on AWS and explore how their underlying, ML apps function together to deliver next-generation computing and learning for eCommerce.

AWS machine learning apps is like a classroom

In the early years of education, the bedrock of the process is teaching children how to learn through cognition tools. Arguably, the cornerstone of all education is based around reading and cognitive functions which is also the main focus in two of the biggest components in machine learning: natural language processing (or NLP) and computer vision.

Google, Microsoft, and Amazon all work using the same premise – the idea is to “teach” machines using predefined algorithms you either feed information or setup self-learning algorithms. In this sense, it’s like human learning as once you can read, you can teach yourself. Machine learning apps read and interpret sales patterns, interactions with AI (i.e. chatbots), information from other digital resources and much more to provide a more secure environment and improved UX.?

Machine learning apps (and more importantly, the backend services) are fed information that’s processed via various algorithms. Let’s look at some of the prebuilt solutions currently on the AWS marketplace that improve eCommerce.

Building an intelligent eCommerce app with the Amazon Machine Learning

The starting point for creating machine learning apps with Amazon can be accomplished by using prebuilt tools from the AWS marketplace. There are specific tools known as machine learning solutions in the AWS Marketplace that are broken down for specific usage scenarios: data preparation, natural language, data science tools, and computer vision.

Currently, there are just shy of 800 different plugins available through Amazon that remove the heavy lifting of building any ML app – meaning, more than just eCommerce –? from scratch. Amazon also offers its own brand of solutions for ML where the main categories that relate to eCommerce are as follows:

Recommendations. The Amazon Personalize tool works to provide users with recommendations based on both their activity and that of other users who have viewed or interacted with various items on a site. Like the eCommerce portion of Amazon, this essentially allows developers to build similar solutions for their apps.

Forecasting. With Amazon Forecast, businesses can unlock more accurate insight into sales predictions by incorporating data beyond sales trends alone. Imagine if you sell lumber – understanding the need for new developments and renovations in your area would be useful to incorporate into sales forecasts, rather than making assumptions based on linear models from past sales alone.

Advanced Text Analytics. Tons of text documents, emails, and other digital items containing text often go unused as the amount of information is too much for a human to parse and simply extracting information (i.e. phrases and keywords) with a program is marginally useful at best as the context is everything. The Amazon Comprehend tool uses NLP that can uncover trends with issues and trends in areas such as customer service, internal communications and beyond by identifying chunks of text and interpreting the information.

If you use Grammarly – and if you’re not, you should be – to check your spelling, grammar, and tone when sending messages, you may notice how it categorizes the tone of your message. This helps identify potential problems when, say, you need to quickly shift from messaging colleagues who are good friends to sending more formal messages to customers or those you don’t consider a good buddy.

Conversational Agents. Many businesses are opting for chatbots that can help customers quickly accomplish a task – it’s not quite a human but far less frustrating than dealing with a series of menus on the phone before maybe speaking with a person. Using the Amazon Lex machine learning model for eCommerce, you can take advantage of automatic speech recognition (ASR) and natural language understanding (NLU) to interpret input from a customer based on how a system is provisioned to quickly lead them to a resolution.

Fraud Detection. You’re naturally going to have some weasely users that try to trick your system through common exploits such as new accounts, guest checkouts, free trial exploitation, and more. In a nutshell, the Amazon Fraud Detector analyzes behavioral patterns against defined high-risk activities to determine the authenticity of a user’s behavior. Customers that appear to be doing “something weird” are flagged (or stopped) before action is taken that violates your terms of service.

Image and Video Analysis. Some of the “best” fraudulent activities occur when a malicious entity spoofs a known contact, merchant, or provider. The Amazon Rekocognition service is mostly used for categorizing images and video but it can also be used in conjunction with the Amazon Fraud Detector to thwart times when someone poses as a trusted entity to extort money from your organization.

This often happens after a phishing attack where a user within your organization or a vendor gets their account “hacked” giving the malicious user free rein to behave however they please. Computer vision in conjunction with NLP can recognize subtleties in messaging, such as when images like brand logos are repurposed on a false page or in emails that intend to siphon money or gain access to information via malware.

Seeing ML in action with existing solutions

There are existing tools on the market that make use of ML, like Shopify, WooCommerce, several other WordPress plugins, among others. For example, Shopify uses ML for fulfillment much the same way as Amazon, including the backend ordering from distribution centers.?

WooCommerce uses ML for similar purposes but extends into regions, namely using NLP, which works in conjunction with the system that shows related products. This is particularly helpful when selling internationally where language affects SEO to the point where it could typically lead a purchaser astray or when character sets for non-Roman lettering send users to dead ends.

Blue Label Labs can use machine learning models for eCommerce to build your app

This is truly just the surface of what Amazon and other providers have to offer in the realm of machine learning. Your apps can enjoy increased security and functionality by introducing mechanisms that learn patterns based on the data you provide or even through unsupervised learning.

Get in touch with Blue Label Labs to learn how we can incorporate machine learning into your applications to offer the best possible experience for your staff and customers.

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