Natural Language Processing with AWS AI Services book cover is on the left; images of the two authors, Mona Mona, top, an AI/ML and former Amazon Web Service employee, and Premkumar “Prem” Rangarajan, bottom, an artificial intelligence/machine learning specialist at AWS, are on the right
Natural Language Processing with AWS AI Services was written by Mona Mona, an artificial intelligence/machine learning specialist and former Amazon Web Service employee, and Premkumar “Prem” Rangarajan, an AI/ML specialist at AWS.

New hands-on guide demonstrates how to implement natural language processing business solutions

Natural Language Processing with AWS AI Services seeks to demystify NLP for just about anyone.

In Ali Baba and the Forty Thieves, Ali Baba overhears one of the thieves utter a magic phrase, “Open sesame,” which opens the mouth of a cave containing treasures.

Premkumar “Prem” Rangarajan, an artificial intelligence/machine learning specialist at Amazon Web Services (AWS), remembers his father reading this story to him as a child. “When I began working with artificial intelligence [AI] and natural language processing [NLP], this story came back to me,” he said. “I realized it was a fictional example of voice activation!”

Rangarajan says that today, AI/NLP can seem almost as magical as the secret code from the folktale.

Artificial intelligence is no longer an inaccessible technology. It’s no longer a career that requires us to study for 10 or 15 years of our lives and get multiple PhDs to begin.
Premkumar “Prem” Rangarajan

“I mean, how do we even make computers, which can only understand ones and zeros, understand your voice?” he asks. “How does it understand that this sound means this with all of the tonal inflections, the accents, the languages? It was so fascinating, and that’s when it began for me. I’m fascinated with using voice for practical applications.”

In an effort to demystify some of that “magic”, Rangarajan and Mona Mona, an AI/ML specialist at Google and former AWS employee, wrote a book. Natural Language Processing with AWS AI Services is a hands-on guide which the authors say can help get any IT professional implementing AI/machine learning solutions before the monthly calendar flips to a new page.

“Artificial intelligence is no longer an inaccessible technology,” says Rangarajan. “It’s no longer a career that requires us to study for 10 or 15 years of our lives and get multiple PhDs to begin.

“Now you can actually understand and choose what you want and directly infuse these algorithms into your applications and build very powerful AI solutions. You don’t have to worry that if you have an idea today it will become a reality a year down the line. It can become a reality in one week, in two weeks.”

Spotting a need

Rangarajan says one area where artificial intelligence and machine learning can generate immediate business intelligence is in customer call centers.

“How can we improve the customer satisfaction scores? How can we understand whether the customer's issue was actually addressed in those conversations? How can we make the agents more efficient? How can we improve call closure rates?” he says. “We have the ability to use AI services to add intelligence to those conversations and to ensure that we address what the customer wants.”

Related content
New features go beyond conventional effect estimation by attributing events to individual components of complex systems.

Mona says soon after she began working at AWS, she recognized the power of the technology stack. “When I saw the power of these tools and I was introduced to some very interesting customer cases, I realized these services can provide natural language processing solutions quickly. You can build a chat bot, or an AI translation solution, or use NLP to do social media analytics. It’s all available to you.”

The authors had previously cowritten about a dozen AWS blog posts on AI/NLP, and from the comments they began to see a need for a new kind of book on NLP.

“We realized that a lot of books talk about the math and the science behind NLP, but there's not a lot of books that showed you how to apply the technology and actually solve the real-world need,” Rangarajan says.

A comprehensive lesson

Natural Language Processing with AWS AI Services begins with an introduction to AWS AI/NLP services, including chapters on AI/NLP stack products such as Amazon Textract and Amazon Comprehend.

The second section of the book demonstrates how NLP can be applied to business solutions, such as improving customer service, monetizing media content, extracting metadata from documents, and specific solutions for healthcare.

Related content
Matt Taddy, vice president of Amazon’s Private Brands business, is the coauthor of Modern Business Analytics: Practical Data Science for Decision Making, a primer for those who want to gain the skills to use data science to help make decisions in business and beyond.

Finally, in section three, the book provides a hands-on guide to putting these solutions into production, including creating workflows and "building secure, reliable, and efficient NLP solutions”. In addition to the book, Rangarajan suggests anyone interested in AI/NLP to set up a free AWS tier account.

“The innovation needed to utilize artificial intelligence and natural language processing is already done by AWS with the AI services stack,” Rangarajan says. “You have AWS Comprehend, Amazon Translate, Amazon Transcribe, et cetera. All you have to do is make an API call to be able to access the intelligence behind those machine learning models.”

Mona notes that the book can be used in different ways by people with different roles within an organization.

Automate document processing using AWS machine learning

“Suppose I'm a business executive. I don't want to read all the code. You could just read the appropriate business problem and solution chapter, the introduction, and the architecture proposed and summary. Then you can pass it on to your technical peers and say, ‘Now I see how it is done and I think this is what we need. Please go and build it,’” she says.

“On the other hand, if I'm a technical person, I will have a different perspective. I will literally read all the code. I can view the videos we have created for each code in the book. So, if you want to implement an end-to-end solution which your manager has given you, now you can go and implement it.”

A resource for career change

Rangarajan says the book is a good primer for someone wanting to transition to focus to AI/NLP, just as he did. He began as an IBM AS 400 programmer and then moved on to become an enterprise application integration architect. During that time he became interested in doing more with machine learning, which led to him joining AWS. Soon he developed an interest in NLP.

Around that time, Rangarajan was asked to work on a project for the celebration of the opening of a new AWS tech hub in Houston. He created an NLP project.

“There was something called ‘Simple Beer Service,’ and this provided an opportunity to upgrade it with Alexa. So, you say to Alexa, ‘Pour me a beer,’ and you use the password. Alexa will then control a Raspberry Pi device, open the beer lines, and pour the beer for you.”

That project (which drew the attention of Houston’s mayor) helped to cement his interest in pursuing NLP — and that interest eventually led to this book. Rangarajan said his own experiences helped shape his approach to the book.

“The book is good for students or working professionals who are interested in moving to an AI/machine learning career. That’s something that’s in demand, so it can be a profitable career move,” Rangarajan says.

The book, combined with the AWS Free Tier accounts, AWS Machine Learning University video tutorials, and, of course, the Amazon AI/NLP technology stack, can help ease entry into the field.

“Amazon's philosophy is that anyone can do this,” Mona says. “Even if you have no basic coding experience, you can still create a scalable application using these AI services. That is the goal, that any student or any IT professional can easily pick these services and implement infused, beautiful, innovative solutions and applications in a week's time. You don’t have to spend a lot of time learning.”

Looking ahead, Rangarajan is writing another book on cloud-native machine learning on AWS. “It’s going to be broad-scale AI and ML and cover the machine learning workflow. So, we are talking about algorithms, neural networks, and how different personnel use machine learning and AI within organizations.”

His mission to help others unlock the potential treasures of machine learning is certainly a goal of which Ali Baba would approve.

Related content

US, NY, New York
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Python is necessary, and experience with SQL and UNIX would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at
FR, Clichy
The role can be based in any of our EU offices. Amazon Supply Chain forms the backbone of the fastest growing e-commerce business in the world. The sheer growth of the business and the company's mission "to be Earth’s most customer-centric company” makes the customer fulfillment business bigger and more complex with each passing year. The EU SC Science Optimization team is looking for a Science leader to tackle complex and ambiguous forecasting and optimization problems for our EU fulfillment network. The team owns the optimization of our Supply Chain from our suppliers to our customers. We are also responsible for analyzing the performance of our Supply Chain end-to-end and deploying Statistics, Econometrics, Operations Research and Machine Learning models to improve decision making within our organization, including forecasting, planning and executing our network. We work closely with Supply Chain Optimization Technology (SCOT) teams, who own the systems and the inputs we rely on to plan our networks, the worldwide scientific community, and with our internal EU stakeholders within Supply Chain, Transportation, Store and Finance. The ideal candidate has a well-rounded-technical/science background as well as a history of leading large projects end-to-end, and is comfortable in developing long term research strategy while ensuring the delivery of incremental results in an ever-changing operational environment. As a Sr. Science Manager, you will lead and grow a high-performing team of data and research scientists, technical program managers and business intelligence engineers. You will partner with operations, finance, store, science and engineering leadership to identify opportunities to drive efficiency improvement in our Fulfillment Center network flows via optimization and scalable execution. As a science leader, you will not only develop optimization solutions, but also influence strategy and outcomes across multiple partner science teams such as forecasting, transportation network design, or modelling teams. You will identify new areas of investment and research and work to align roadmaps to deliver on these opportunities. This role is inherently cross-functional and requires an ability to communicate, influence and earn the trust of science, technical, operations and business leadership.
US, WA, Bellevue
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Python is necessary, and experience with SQL and UNIX would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at Key job responsibilities Estimate econometric models using large datasets. Must know SQL and Matlab.
US, WA, Seattle
The AWS AI Labs team has a world-leading team of researchers and academics, and we are looking for world-class colleagues to join us and make the AI revolution happen. Our team of scientists have developed the algorithms and models that power AWS computer vision services such as Amazon Rekognition and Amazon Textract. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. AWS is the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems which will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. Our research themes include, but are not limited to: few-shot learning, transfer learning, unsupervised and semi-supervised methods, active learning and semi-automated data annotation, large scale image and video detection and recognition, face detection and recognition, OCR and scene text recognition, document understanding, 3D scene and layout understanding, and geometric computer vision. For this role, we are looking for scientist who have experience working in the intersection of vision and language. We are located in Seattle, Pasadena, Palo Alto (USA) and in Haifa and Tel Aviv (Israel).
US, WA, Seattle
Amazon Prime Video is changing the way millions of customers enjoy digital content. Prime Video delivers premium content to customers through purchase and rental of movies and TV shows, unlimited on-demand streaming through Amazon Prime subscriptions, add-on channels like Showtime and HBO, and live concerts and sporting events like NFL Thursday Night Football. In total, Prime Video offers nearly 200,000 titles and is available across a wide variety of platforms, including PCs and Macs, Android and iOS mobile devices, Fire Tablets and Fire TV, Smart TVs, game consoles, Blu-ray players, set-top-boxes, and video-enabled Alexa devices. Amazon believes so strongly in the future of video that we've launched our own Amazon Studios to produce original movies and TV shows, many of which have already earned critical acclaim and top awards, including Oscars, Emmys and Golden Globes. The Global Consumer Engagement team within Amazon Prime Video builds product and technology solutions that drive customer activation and engagement across all our supported devices and global footprint. We obsess over finding effective, programmatic and scalable ways to reach customers via a broad portfolio of both in-app and out-of-app experiences. We would love to have you join us to build models that can classify and detect content available on Prime Video. We need you to analyze the video, audio and textual signal streams and improve state-of-art solutions while being scalable to Amazon size data. We need to solve problems across many cultures and languages, working alongside an operations team generating labels across many languages to help us achieve these goals. Our team consistently strives to innovate, and holds several novel patents and inventions in the motion picture and television industry. We are highly motivated to extend the state of the art. As a member of our team, you will apply your deep knowledge of Computer Vision and Machine Learning to concrete problems that have broad cross-organizational, global, and technology impact. Your work will focus on addressing fundamental computer vision models like video understanding and video summarization in addition to building appropriate large scale datasets. You will work on large engineering efforts that solve significantly complex problems facing global customers. You will be trusted to operate with independence and are often assigned to focus on areas with significant impact on audience satisfaction. You must be equally comfortable with digging in to customer requirements as you are drilling into design with development teams and developing production ready learning models. You consistently bring strong, data-driven business and technical judgment to decisions. You will work with internal and external stakeholders, cross-functional partners, and end-users around the world at all levels. Our team makes a big impact because nothing is more important to us than pleasing our customers, continually earning their trust, and thinking long term. You are empowered to bring new technologies and deep learning approaches to your solutions. We embrace the challenges of a fast paced market and evolving technologies, paving the way to universal availability of content. You will be encouraged to see the big picture, be innovative, and positively impact millions of customers. This is a young and evolving business where creativity and drive will have a lasting impact on the way video is enjoyed worldwide.
US, CA, Palo Alto
Join a team working on cutting-edge science to innovate search experiences for Amazon shoppers! Amazon Search helps customers shop with ease, confidence and delight WW. We aim to transform Search from an information retrieval engine to a shopping engine. In this role, you will build models to generate and recommend search queries that can help customers fulfill their shopping missions, reduce search efforts and let them explore and discover new products. You will also build models and applications that will increase customer awareness of related products and product attributes that might be best suited to fulfill the customer needs. Key job responsibilities On a day-to-day basis, you will: Design, develop, and evaluate highly innovative, scalable models and algorithms; Design and execute experiments to determine the impact of your models and algorithms; Work with product and software engineering teams to manage the integration of successful models and algorithms in complex, real-time production systems at very large scale; Share knowledge and research outcomes via internal and external conferences and journal publications; Project manage cross-functional Machine Learning initiatives. About the team The mission of Search Assistance is to improve search feature by reducing customers’ effort to search. We achieve this through three customer-facing features: Autocomplete, Spelling Correction and Related Searches. The core capability behind the three features is backend service Query Recommendation.
US, CA, Palo Alto
Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. The Ad Response Prediction team in Sponsored Products organization build advanced deep-learning models, large-scale machine-learning (ML) pipelines, and real-time serving infra to match shoppers’ intent to relevant ads on all devices, for all contexts and in all marketplaces. Through precise estimation of shoppers’ interaction with ads and their long-term value, we aim to drive optimal ads allocation and pricing, and help to deliver a relevant, engaging and delightful ads experience to Amazon shoppers. As the business and the complexity of various new initiatives we take continues to grow, we are looking for energetic, entrepreneurial, and self-driven science leaders to join the team. Key job responsibilities As a Principal Applied Scientist in the team, you will: Seek to understand in depth the Sponsored Products offering at Amazon and identify areas of opportunities to grow our business via principled ML solutions. Mentor and guide the applied scientists in our organization and hold us to a high standard of technical rigor and excellence in ML. Design and lead organization wide ML roadmaps to help our Amazon shoppers have a delightful shopping experience while creating long term value for our sellers. Work with our engineering partners and draw upon your experience to meet latency and other system constraints. Identify untapped, high-risk technical and scientific directions, and simulate new research directions that you will drive to completion and deliver. Be responsible for communicating our ML innovations to the broader internal & external scientific community.
US, CA, Palo Alto
We’re working to improve shopping on Amazon using the conversational capabilities of large language models, and are searching for pioneers who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry. You'll be working with talented scientists, engineers, and technical program managers (TPM) to innovate on behalf of our customers. If you're fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey!"?
US, CA, Santa Clara
AWS AI/ML is looking for world class scientists and engineers to join its AI Research and Education group working on foundation models, large-scale representation learning, and distributed learning methods and systems. At AWS AI/ML you will invent, implement, and deploy state of the art machine learning algorithms and systems. You will build prototypes and innovate on new representation learning solutions. You will interact closely with our customers and with the academic and research communities. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. Large-scale foundation models have been the powerhouse in many of the recent advancements in computer vision, natural language processing, automatic speech recognition, recommendation systems, and time series modeling. Developing such models requires not only skillful modeling in individual modalities, but also understanding of how to synergistically combine them, and how to scale the modeling methods to learn with huge models and on large datasets. Join us to work as an integral part of a team that has diverse experiences in this space. We actively work on these areas: * Hardware-informed efficient model architecture, training objective and curriculum design * Distributed training, accelerated optimization methods * Continual learning, multi-task/meta learning * Reasoning, interactive learning, reinforcement learning * Robustness, privacy, model watermarking * Model compression, distillation, pruning, sparsification, quantization About Us Inclusive Team Culture Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Work/Life Balance Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives. Mentorship & Career Growth Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, WA, Seattle
Do you want to join an innovative team of scientists who use machine learning to help Amazon provide the best experience to our Selling Partners by automatically understanding and addressing their challenges, needs and opportunities? Do you want to build advanced algorithmic systems that are powered by state-of-art ML, such as Natural Language Processing, Large Language Models, Deep Learning, Computer Vision and Causal Modeling, to seamlessly engage with Sellers? Are you excited by the prospect of analyzing and modeling terabytes of data and creating cutting edge algorithms to solve real world problems? Do you like to build end-to-end business solutions and directly impact the profitability of the company and experience of our customers? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Selling Partner Experience Science team. Key job responsibilities Use statistical and machine learning techniques to create the next generation of the tools that empower Amazon's Selling Partners to succeed. Design, develop and deploy highly innovative models to interact with Sellers and delight them with solutions. Work closely with teams of scientists and software engineers to drive real-time model implementations and deliver novel and highly impactful features. Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. Research and implement novel machine learning and statistical approaches. Lead strategic initiatives to employ the most recent advances in ML in a fast-paced, experimental environment. Drive the vision and roadmap for how ML can continually improve Selling Partner experience. About the team Selling Partner Experience Science (SPeXSci) is a growing team of scientists, engineers and product leaders engaged in the research and development of the next generation of ML-driven technology to empower Amazon's Selling Partners to succeed. We draw from many science domains, from Natural Language Processing to Computer Vision to Optimization to Economics, to create solutions that seamlessly and automatically engage with Sellers, solve their problems, and help them grow. Focused on collaboration, innovation and strategic impact, we work closely with other science and technology teams, product and operations organizations, and with senior leadership, to transform the Selling Partner experience.