Build a Better Business Strategy with Machine Learning
by Kirk Rafferty, Senior Solution Architect, Rackspace Technology
Machine learning (ML) is more than just the buzzword du jour, and it’s gone from computer science labs to the real world at lightning speed. There are two types of enterprises out there right now. There are the organizations that have incorporated ML into their operations, and there are the organizations that will soon be playing catch-up.
Machine learning isn’t a cryptic science anymore. In fact, in 2016, The Harvard Business Review was making the case that your business needs to make ML part of your enterprise tool kit, like yesterday — and that was almost six years ago. Today, big logos like Netflix, Microsoft, Snapchat and Uber are all using ML technologies in one form or another to effectively automate marketing and customer retention, prevent fraud, and generate a deep, automated analysis of their data.
Our recent Rackspace Technology® AI/ML Annual Research Report 2022 found that 72% of IT leaders worldwide see artificial Intelligence (AI) and machine learning as critical elements of their business strategies. In addition, they’re seeing measurable results from their AI/ML programs and believe they are gaining a competitive advantage in the marketplace. We found that 70% of all respondents reported positive impacts of AI/ML on brand awareness and reputation, revenue generation, and expense reduction.
Incorporating a new technology, especially something seemingly as mysterious as ML, can be daunting. Sometimes the challenge is understanding where and how to start while identifying ML’s benefits for your business’s bottom line.
Here are five ways enterprise operations use machine learning services and tools to generate insight from large and often disparate data sets.
You likely have vast troves of data generated from sales leads, market research, trend analyses and other sources living in multiple places like databases, data warehouses, cold storage and object storage. Tools such as Amazon Comprehend let you mine business and call center analytics, create power custom search engines, and process documents for sentiment and other analysis, all against multiple sources of data.
With additional Amazon Web Services (AWS) tools, you can quickly build and train predictive models to determine marketing campaign outcomes before spending time, money and resources on them.
You’ve worked hard and spent time and capital to acquire your customer base. Losing them is costly. Using Amazon Machine Learning, you can build models using data you already have to generate profiles on customers in that unhappy red zone.
Amazon has taken most of the work out of creating these models, allowing you to plug in your data and quickly generate results. Your models can be as simple or complex as you need them to be, and you’re free of the grunt work involved in creating algorithms and deep prediction analysis. It’s all handled for you, and it all works with your data.
With the release of Amazon SageMaker in 2017, developers could use pre-trained ML models or their data to analyze and generate predictions using a point-and-click interface. Since then, it’s only gotten better with the addition of more robust features.
Now, data scientists and developers can quickly prepare, build, train and deploy high-quality machine learning models with a broad set of capabilities. Businesses use these models to enter untapped markets, optimize manufacturing and transform their businesses using data instead of guesswork.
Image and Video Analysis
Using Amazon Rekognition, organizations are turning photos and videos into searchable content. The automation utilizes re-trained or customized computer vision models, text detection and custom object detection. Data can then be gathered and analyzed in the same way we query documents and databases.
Custom labels can be generated and applied to videos or images while moderating media for inappropriate or unwanted content. By filtering this data, you’re able to turn gigabytes and terabytes of digital media into searchable and analyzable datasets usable across your organization.
Your business can be doing everything right and still fall prey to abuse and fraud. Amazon Fraud Detector is a fully managed and optimized AWS service that helps you identify potentially fraudulent activities and transactions so you can stop them at the source.
Amazon Fraud Detector can flag suspicious online payments, uncover fraudulent new accounts, enforce verification checks such as phone and email validation, and prevent loyalty program and trial offerings abuse. It works by automating the creation of machine learning models that continuously learn and continuously improve your ability to stop fraud at the source. And for deeper analysis, Amazon Fraud Detection is fully integrated with Amazon SageMaker.
These are just a few examples of what integrating Amazon’s Machine Learning suite of tools and services can do for your organization. And even if it looks great, it might still seem a bit daunting. Fortunately, Onica by Rackspace Technology® has the expertise to help make your machine learning business strategy possible and pain-free.
For more insights into how IT decision-makers invest in artificial intelligence and machine learning, download our AI/ML Annual Research Report.