Getting Started With AWS Machine Learning

April 18, 2020

What is AWS Machine Learning?

Amazon Web Services (AWS) Machine Learning is a set of artificial intelligence models designed to optimize a business’s applications and workflows. Amazon’s platform offers a variety of services from computer vision and language to recommendations and forecasting. Pre-trained AI services can be integrated into a company’s applications to improve work functions, provide custom recommendations, update its contact centers, provide security support, and augment customer engagement.

Here at OmniLegion, we’ve partnered with AWS to help companies harness the power of AI and machine learning and drive dynamic growth with the use of Amazon Connect. Because we value above-and-beyond customer service and operational excellence at OmniLegion, we’ve worked hard to apply incredible AWS technology to the end user experience. If you’re interested in high tech solutions with seamless integration into your existing business applications, read on to learn how to get started in AWS machine learning.

Why Use AWS?

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For any individuals or businesses weighing AI and machine learning options on the market, it pays to research which platform will best serve your unique needs. There are a few other platforms to choose from other than AWS, such as Google Cloud and Microsoft Azure. Which of these platforms is best for you and your company?

We’ve explored the benefits of each of these platforms in the area of machine learning to help you find what works best for your company. In what ways do their services and features differ and what are their best strengths? Below are some of our findings in evaluating and comparing the API (application programming interface) features of AWS vs. Google Cloud vs. Microsoft Azure.

AWS v. Microsoft Azure

Microsoft Azure offers its machine learning APIs through a program called Cognitive Services. For purposes of exploring machine learning functions, APIs can be divided into speech processing techniques, language and textual analysis, and image and video recognition. 

Azure applies four APIs to accomplish natural language processing and natural speech recognition operations: translator speech API, Bing Speech API (text to speech and vice versa conversions), speaker recognition API, and custom speech services based on its own data and models. The platform’s language APIs include a text analysis API, Bing Spell Check, text translator API, web language model API, and a linguistic analysis API. Microsoft offers a flexible bot development toolset, which allows users to build, test, and deploy bots using different programming languages.

In comparison, Amazon’s machine learning APIs go even deeper when it comes to textual and language analysis. Amazon Lex embeds chatbots into a business’s applications and applies natural language processing and automatic speech recognition. This highly automated API can be fully integrated into a company’s existing system. A complex chatbot tool like Lex is a boon to companies hoping to increase customer engagement and improve consumer satisfaction within a short time frame. 

Amazon’s language APIs also include Amazon Transcribe which recognizes spoken text. The tool can recognize a number of speakers and functions even in low-volume situations. Used frequently to contribute to call-center data or organizing archives, this multifunctional tool helps businesses stay on task and gather valuable data as well. Amazon Polly functions much like Alexa and supports 29 languages. Amazon’s Translate and Comprehend APIs round off its extensive speech and language functions, which allow for neural-based translation and  detection of valuable big textual data respectively.

Microsoft’s image and video APIs are included in its Vision package, which includes: computer vision that recognizes objects, action, text, and colors in images, content moderator, facial recognition, emotional recognition, custom vision functions, and video indexing tool.

Amazon’s machine learning video capabilities, included in Amazon Rekognition, overlap with Microsoft’s APIs but have the added ability of detecting both general and complex action in videos.

AWS v. Google Cloud

Google Cloud employs Cloud AutoML for its speech and language capabilities. Most of its speech and language recognition functions are similar to Microsoft and Amazon APIs, such as recognizing intent and sentiment, defining entities, analyzing sentences, and categorizing topics. Google Cloud’s language APIs are most recognized for a high number of languages supported by the platform. Google’s video recognition technology is still in its early stages so it is not yet as developed as AWS’ or Microsoft’s APIs.

In comparison, AWS has a voice recognition ability which Google Cloud does not yet have. Further, while AWS’ language and translation functions are wholly developed, Google’s translation API is now in beta and is still going through updates at the moment. Finally, AWS also offers cloud-based disaster services while Google offers out-of-the-box backup services.

AWS vs. Google Cloud vs. Microsoft Azure

While each platform has its own individual strengths, notably Google for its language capabilities and Microsoft for its bot building flexibility, we’ve found that AWS’ machine learning functions are a great choice for businesses seeking integrative solutions. Amazon developed its machine learning APIs as a solution to its own operational challenges as a business and learned through experience what AI functions are necessary to help the company. 

When Amazon discovered its retail business needed a contact center to give its customers a personalized experience, it created Amazon Connect to improve agent performance, lower costs, and simplify contact center operations. This omnichannel cloud center is now available to other businesses hoping to achieve the same goals. At OmniLegion, we’ve partnered with AWS Amazon Connect to help your company deploy conversational AI, virtual assistants, and chatbots for your own business needs. Learn more about what we do here.

How Can Amazon Connect Improve Customer Experience?

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Amazon Connect provides a modern and cost-effective alternative to traditional contact centers. A number of companies are already transitioning from legacy contact centers to an omnichannel platform to improve customer experience. With a recent Gartner survey finding that positive customer experience drives over two-thirds of customer loyalty,

(more so than pricing and brand combined) companies are inclined more than ever to seek efficient solutions to improve consumer interactions.

Consider the example of a customer calling through to a company’s legacy contact center. The caller must be connected from department to department in order to resolve an issue with your company. As the time to resolve lengthens, and the caller must explain the same situation over and over again to various department agents, the customer’s frustration grows, resulting in an overall dissatisfactory experience with your company. More likely than not, an unpleasant experience like the one described may deter the customer from returning.

Comparatively, an omnichannel solution like AWS Connect allows information to be transferred seamlessly from one department to another, creating a smoother experience for the customer and a likely more pleasant customer experience. With market studies showing it costs more to gain new customers rather than retaining existing customers, it pays for companies to invest in a better interaction experience like the one offered by AWS Connect. If you’re ready to make the switch to improve customer relations at your own business, check out what we do to facilitate AWS Connect functions for your company.

How Can AWS Connect Improve Operations?

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Amazon Connect estimates a savings of 80% using its services compared to traditional legacy contact center services. Pricewise, most legacy contact centers charge a flat monthly or hourly rate. With a cloud-based platform like Amazon Connect, users only pay for the time spent on consumer interaction, plus any associated messaging and telephonic fees. 

As a result, any company using AWS’ platform is only paying for services that are directly linked to potential profit for the business rather than paying out flat fees to a legacy contact center. Ultimately, switching out a legacy contact plan and investing in a plan with AWS Connect will ensure effective use of valuable company resources.

Further, with an omnichannel system like Amazon Connect, customer challenges are resolved quickly and efficiently, leading to less time and money spent on data center fees. AWS’ highly automated platform frees up your company’s operational resources to be spent on improving other aspects of your business, such as branding and product quality. 

Finally, AWS’ advanced machine learning techniques grants your company the data it needs to make operational decision making easier and better informed. Some of the data Amazon Connect handles includes:

  • Resources and configurations, including queues, contact flows, users, and routing profiles.
  • Contact metadata, including connection time, handle time, source number, destination number, and user-defined contact attributes.
  • Agent-related performance data, including login time, status changes, and contacts handled.
  • Phone call audio streams, including call recordings if this feature is enabled.
  • Chat transcripts, which are included if this feature is enabled.

Amazon Connect’s AI capabilities can transcribe calls and show caller sentiment in real-time, allowing your company to mine customer engagement data to understand insights and spot trends. These machine learning capabilities integrated into Amazon Connect empowers contact center managers to effectively analyze the trends, sentiments, and compliance risks of customer interactions. With this information in hand, your company can properly train agents, reproduce successful customer communications, and gather valuable company and product feedback.

What Industries Can AWS Machine Learning Be Applied To?

Factory building lit up at night

AWS Connect has been helpful to companies in a myriad of industries, including banking, financial management, power and utilities, home appliances, IT, digital content distribution, hospitality, food and beverage, retail, and government and community organizations. The flexible and widespread capabilities of AWS machine learning can be applied in a manner customized to each business’ needs. Companies can use Amazon Connect with AWS services toward development, data storage, database functions, AI functions, analytics, messaging, security, and management.

Take, for example, lekker Energie, a company that provides supraregional gas and utility services on the German energy market. As a customer and service-oriented business, lekker deployed AWS Connect to keep up with its customers’ expectations. The company reports substantial benefits from incorporating AWS Connect into its system: AWS automation frees up front and back office agents to accomplish higher-value tasks, AWS machine learning allows the company to provide customized recommendations for its customers, and AWS Connect’s data gathering capabilities allow lekker to improve customer identification, measure customer satisfaction, and analyze unmet customer needs. If you’re curious how AWS machine learning can help your company’s customer service needs, check out how OmniLegion can help.

How Can CTI Integration Streamline Company Operations?

Black and brown headset next to laptop

CTI Integration stands for computer technology integration and is chiefly used to connect computer technology with telephone communications. CTI allows customer service agents to look up customer information quickly and efficiently and determine the proper department for a caller on an expedited basis.

Agents can therefore spend less time on time-wasting tasks, such as searching through voluminous customer records, and devote more time to personalized customer interaction and resolving the customer’s unique needs. CTI integration creates a more effective customer experience with reduced handle times and increased customer satisfaction. At Omni Legion, we provide CTI integration services for Amazon Connect so that your business can make a smooth transition from legacy system to a more modern and advanced technology solution.

Key Takeaways

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An exploration of AWS’ machine learning capabilities proves why Amazon Connect’s services are a competitive leader in the cloud contact center market. With highly developed AI and bot features that can be seamlessly integrated into a company’s system and versatility across numerous fields and industries, AWS Connect can help any company achieve its customer service and operational goals within a short period of time. But what does your business need to get started on AWS machine learning?

For companies and contact center managers hoping to devote their time to what matters most, customer satisfaction, look no further than OmniLegion to help bridge the gap. Our skills, experience, and CTI Integration methods save your company valuable time by deploying Amazon Connect services to serve your needs and make your vision a reality. Give your company the incredible opportunity to grow and expand with up-to-date machine learning technology. Check out our innovative solutions here.

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