Tapping into the Power of Machine Learning and AI on AWS
The concept of artificial intelligence (AI) has long been sensationalized to a fantastical degree in pop culture and movies.
You’d be forgiven for feeling as though it’s part of some sci-fi narrative far removed from the business world and the goals your organization is looking to achieve.
The reality, however, is that the benefits offered by AI and machine learning (ML) aren’t just relevant to your organization, but very much within reach: AI and ML are relatively simple concepts that can have a positive, significant impact on your business, no matter what industry you’re operating in.
AI and ML aren’t just buzzwords, but powerful tools that your organization can leverage to reach new levels of productivity and innovation. But what’s the difference between the two?
In a nutshell, AI refers to a type of computer science that focuses on the creation and development of machines that can mimic human characteristics.
ML, on the other hand, is a branch of AI, centered around algorithms that can learn from the data you provide without much human intervention.
These algorithms are ‘alive’ in the sense that they can improve themselves and evolve over time when exposed to new information. This helps data scientists to sort through huge amounts of data more efficiently and cost-effectively than any traditional alternative.
Getting started with AI
AI and machine learning aren’t just for big businesses anymore. There are a range of types of AI and machine learning services that can help with everything from transcribing text and organizing images to gathering information from customers/surfacing crucial information from reports.
Alexander Konduforov, a Machine Learning Engineer and Data Science Competence Leader at AltexSoft, believes that adopting AI is a natural progression for the business world.
“There is no doubt that Artificial Intelligence (AI) is going to have a transformational impact on business similar to the one that computers and software made several decades ago,” says Alexander. “AI can help businesses by unveiling hidden insights about their operations from the data, improving workflow and KPI effectiveness, augmenting and automating decisions”.
“For example, such a system can help marketing specialists to use a personalized approach to improve customer conversion. Instead of creating segments manually which is impossible to achieve with thousands of customers, a system will analyze their data and segment them automatically. Another example is fraud detection which can’t be fully automated without applying AI-based algorithms and many more.”
So why should a business think about developing its own intelligent solutions? According to Timo Böhm, a Senior Consultant for Data Science & AI at b.telligent, it’s all about being able to address specific business needs. “The main decision to make is between pre-built solutions from specialized vendors and own development efforts,” says Timo.
“In most cases, pre-built solutions will fail to represent the idiosyncrasies of the business model. Since both the technology and skills to develop tailored solutions are available, there are few cases where such compromises are necessary.” — Timo Böhm
Alexander agrees: “In the case when a business creates its own software product, AI-based features could potentially make thousands of customers much happier and bring additional value and competitive advantage.”
The benefits of harnessing AI are already showing up on the bottom lines of those who’ve moved to implement it. “A recent study from McKinsey Global Institute reveals that series AI adopters report a 3% to 15% higher profit margin, depending on the industry, compared to non-adopters,” says Alexander. “Another report from Deloitte Insights shows that 80% of organizations that invest in AI technologies receive from 10% up to 40%+ return on investment. That’s another reason why businesses should definitely start exploring AI.”
Before we get a closer look at how AWS can deliver the tools an organization needs to create cutting-edge AI tools, let’s examine what this kind of journey typically looks like. If you’re looking to invest in AWS’s AI services, you’ll need to:
- Know where you are versus where you’d like your organization to be
- Identify the pain points preventing you from getting there
- Explore how solid predictions can help you get where you need to be
- Decide whether or not you’ll be using an AWS Partner Network (APN) partner (the majority of businesses choose to work with an APN partner with the Machine Learning Competency to their name)
- Take practical action with a comprehensive, company-wide AI strategy
Generally speaking, the problems most modern businesses need to address boil down to three categories; regression, classification, and clustering.
- Regression involves the prediction of a number. For example, how much revenue a product will generate based on a set number of variables.
- Classification predicts which group is most likely to exhibit certain behaviors based on your set criteria. For example, will this customer be active in 12 months based on this demographic information?
- And finally, clustering leads to the discovery of new groups. For example grouping customers based on a set of criteria to find common traits and patterns of behavior to help identify any underlying relationship between them.
What AWS and AI can do for you
According to a recent report by the International Data Corporation (IDC), global spending on AI solutions is set to hit a whopping $77.6 billion by the end of 2022; the same study also predicts that AI and ML could unlock an additional $2.6 trillion to the value of Marketing and Sales, and no less than $2 trillion in the manufacturing and supply chain planning industries.
Powering this unprecedented worldwide growth is the emergence of new technologies that far surpass the capabilities of existing systems when it comes to aggregating data, integration and analysis, and scalability.
In recent years, Amazon Web Services has reorganized its entire business structure and product offering around AI and ML solutions.
“Amazon provides quite a broad set of services for Machine Learning for solving different types of problems,” says Alexander.
“The choice of a solution largely depends on team skills and business challenges. For example, you can use the AWS Forecast for financial planning and sales prediction without prior machine learning knowledge, while Amazon SageMaker is a more advanced tool that facilitates building various types of AI solutions and serves to automate the workflow. All these pre-trained AI services incorporate a huge amount of ML work already done by Amazon researchers and can be easily integrated into a product or particular workflow.” — Alexander Konduforov
“The products and services provided by tech giants like Amazon allow organizations to start their Machine Learning projects with much lower barriers for entry and spend less time and budget on building new products or automating existing workflows. It must be noted though that for some types of problems it makes sense to train custom models using Amazon SageMaker or even plain Python and TensorFlow instead of using existing pre-trained APIs because of higher flexibility and better results that can be achieved from custom ML modeling.”
Maximizing the customer experience
Tapping into this kind of tech will help optimize the customer experience like never before, whether that’s offering a more personalized customer journey, automating content moderation online, improving the quality of scientific or medical analytics, or forecasting demand more accurately in a move to cut costs more effectively.
The possibilities are endless (and dependent entirely on specific business requirements and organizational goals) and can include:
- Sophisticated image/video analysis
- Personalized recommendations
- Virtual assistants
- Forecasting without the need for deep expertise in machine learning
- Creating complex human-like functionality (e.g. chatbots)
As specialist AWS ML Experts, we’ve seen a marked increase in the number of businesses, from startups to larger enterprises, investing in AI to help drive their operations forward.
This increase in popularity is not simply down to AI being something shiny and new, but because it’s become much more financially viable.
Through a combination of the capabilities offered by cloud computing and general advancements in software and tech, it’s now cheaper than ever to make predictions based on your company’s most valuable asset: data.
Using predictions to drive business outcomes
If AI had its own family motto, it would be something like: “optimize the present and stay two steps ahead of the future”. AI analyzes existing data, spots patterns and trends, and makes better decisions further down the line. These predictions lie at the core of the value offered by AI and ML.
Essentially, making predictions involves using the data you’ve gathered to produce what can be described as an extremely educated forecast, shaped by your organization’s historical data.
These predictions allow you to make better-informed business decisions, generate insights into untapped opportunities, and overcome day-to-day operational challenges in a more streamlined way.
Of course, for AI to actually be useful and drive innovation, it needs to be easy to use and financially accessible. Enter AWS.
AWS Machine Learning services
AI, ML, and deep learning offer huge economic potential for businesses across a whole range of industries—but only if you have the resources, skilled professionals, and an expansive business case necessary to implement it effectively.
ML processes are historically expensive to run, but that’s where providers like AWS come in. Thanks to the cloud, these cutting-edge tools have become not only financially feasible, but a necessity for any organization looking to compete and stay ahead in their industry.
SageMaker gives developers and data scientists the tools they need to build, train, and deploy ML models quickly and cost-effectively. SageMaker is fully managed and handles the ML workflow from start to finish, allowing you to get your models into production fast and with far less resource.
“The general storage (AWS S3) and compute services (AWS EC2), combined with serverless functionality (AWS Lambda) and workflows (AWS Stepfunctions), can go a very long way already,” Timo says. “However, there is some additional value in using the specialized machine learning infrastructure, AWS SageMaker, too. The prebuilt APIs are especially useful for problems that are very hard to solve and not in the core of the business model, such as translation via AWS Translate.”
Amazon SageMaker Ground Truth
This variation of Amazon SageMaker allows you to build training datasets for ML quickly and with unrivaled accuracy, offering straightforward access with built-in workflows and interfaces for everyday labeling tasks.
Amazon SageMaker Neo
Amazon SageMager Neo allows developers to train up ML models just once and deploy them anywhere in the cloud. It optimizes these models to run at double the speed while using less memory without compromising on accuracy.
This ML-powered service makes finding insights and identifying relationships in text an absolute cake-walk. Comprehend takes unstructured data and uses natural language processing to identify languages, key phrases, individuals, locations, people, events, or brands together with positive or negative sentiments to create a set of files organized by topic.
It can be used on all kinds of content from customer emails to support tickets, product reviews, call center recordings, social media metrics, and more.
Amazon Comprehend Medical
This product is tailor-made for the medical sphere. Amazon Comprehend Medical seeks out and extracts vital medical details from unstructured text. The service allows users to identify key information, including medical conditions, prescribed medication, and dosages from a range of sources.
This fully managed ML-based tool serves up incredibly accurate forecasts by analyzing historical time series data. Rolling together time series data with other variables, Forecast autonomously examines data sets, identifies only what’s meaningful, and uses this to generate a model capable of making predictions on average up to 50% more accurate than those based on just time-series data.
Amazon Lex for AI Chatbots
Over 65 million companies globally use social media as part of a wider marketing strategy, so it comes as no surprise that AI chatbots are making waves across all kinds of industries.
These chatbots have proven to be highly effective when it comes to catching sales and marketing opportunities which would have otherwise slipped beneath notice. Amazon Lex aims to help more companies take advantage of chatbots.
Amazon Lex allows you to create sophisticated conversational interfaces into any app, covering both voice and text. It has all the deep learning functionalities of automatic speech recognition combined with natural language understanding to identify intent more accurately and create more engaging, organic user experiences.
Amazon Personalize allows companies to create custom recommendations for their customers, boosting engagement and add-on sales by serving up tailored product or content recommendations, search results, and targeted ads.
This Text-to-Speech (TTS) service transforms text into lifelike speech, allowing users to build apps that talk and create completely new breeds of speech-enabled products. Equipped with dozens of realistic voices across a range of languages, Polly even has a specialist “newscaster” voice designed for news narration services.
Amazon Rekognition makes it easy to add image and video analysis to your applications. Simply submit an image or video to the Rekognition API, and the service can identify objects, people, text, scenes, and activities, as well as to detect any inappropriate content.
Amazon Rekognition also provides highly accurate facial analysis and facial recognition on images and video for a wide variety of user verification, people counting, and public safety use cases.
A super-powered version of standard optical character recognition (OCR) tools, Amazon automatically extracts text and data from scanned documents. Textract can also recognize and transcribe the contents of fields in forms and information in tables.
Eliminating the need for time-consuming manual transcription of hard copy documents, Textract uses machine learning to read text and can process millions of pages in a matter of hours.
Textract can also generate smart search indexes, create automated approval workflows, and ensure compliance with your archival procedure by flagging data that might need to be redacted.
Amazon Transcribe is an automatic speech recognition tool that can be used to analyze and transcribe both existing audio files and live audio like video streams or calls.
The service timestamps each word so audio can easily be found within the source material. Transcribe uses deep learning to constantly improve accuracy, add punctuation, and format text, meaning minimal additional editing for you.
Are AI and ML for you?
Still think you don’t need to be taking advantage of AI and machine learning in your business?
Timo suggests thinking outside the box to find processes to save time and money through automation. “The best way to find surprising use cases for AI is to look for statements like “there is no way to automate that” or “we will always need a person to do this”, advises Timo. “In many of these cases, AI can be used to completely or partly automate a process.”
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