Therefore, it has become a priority to expand the Amazon Web Services technology with this type of mechanism, as well as, create new services facilitating the implementation of artificial intelligence in the IT environment of customers. Yet, before jumping into the extensive portfolio of available solutions, let us elaborate more on AI, Machine Learning, and Deep Learning.

Artificial Intelligence (AI)

Artificial intelligence is a broad concept. Overall, it aims to mimic human decision-making processes and perform complex tasks in a more human way than ever before. This is understood as, inter alia, the ability to adapt independently to changing conditions, and also includes machines that can perform tasks characteristic of human intelligence and do it faster, and more efficiently than humans. It is also worth noting that this sector is a much broader concept than machine learning. Behavioral algorithms, virtual agents, or autonomously powered, self-propelled vehicles with predictive capabilities – this is but a few of examples of the use of artificial intelligence today.

Machine learning (ML)

Machine learning is a technique in the field of computer science and statistical modeling that allows a computer application based on independent analysis – without the need to program it – to predict the result or make a decision. This technology is closely related to data analytics, data mining, and data science engineering. Both machine learning and data mining use mathematical algorithms to explore data and search for patterns. Machine learning uses algorithms to detect patterns in data sets and adjusts program performance accordingly. Predictive analytics services, regardless of all big data challenges, help developers and data researchers use machine learning in new ways.

Deep learning

Deep learning is one of the varieties of artificial intelligence (AI), a subcategory of machine learning – a technique involving the creation of neural networks, the main task of which is to improve the techniques of computer vision, voice recognition and natural language processing. Simply put, deep learning can be seen as a way to automate predictive analytics. Deep learning applications cover many types of big data analytical applications, in particular those focused on natural language processing (NLP), translation of foreign languages, medical diagnostics, stock transactions, security services, and image recognition.

Selection of services offered by AWS

Amazon Lex

Amazon Lex is an AWS service that allows you to build conversational interfaces into text and speech applications. Due to the use of automatic speech recognition and its conversion into the text, as well as, natural language understanding, it allows the user to experience a very real interaction. It is widely used in the implementation of chatbots that improve communication and customer service, as well as, allow you to automate simple tasks. It does not require infrastructure management. In combination with other AWS services such as Lambda and Polly, it can handle various business scenarios.

Amazon SageMaker

Amazon SageMaker is dedicated primarily to programmers and data scientists who want to build, train and implement machine learning models. Amazon SageMaker algorithms adopt a stream computing model that can process an infinite amount of data. The algorithm goes through the data set only once and then takes a persistent memory trace. This solution excludes storing, shuffling, and multiple reading of data, as well as, random access to individual records. Besides, streaming algorithms natively retrieve data from short-lived sources, while traditional machine learning algorithms typically retrieve data from persistent sources such as local disks. As a result, thanks to that a training task that takes ten hours on a single computer can be run on 10 computers and completed within an hour. You can reduce learning time even further by replacing your device with one with GPU support.

Amazon Rekognition

Amazon Rekognition allows advanced image processing, which in the past would require me to build quite a sizable database and implement algorithms that were known and last used in my studies. Now the user can write and run an application in less than an hour, and the complicated algorithms come down to calling one AWS SDK function. Rekognition enables the analysis of static images and video sequences (also streaming). The service can identify objects, people, text, scene, and activity depicted in the image. It’s also great for filtering out inappropriate content posted by users. In combination with AWS Lambda (Function as a Service), API Gateway, and S3, we can build an advanced application without running any virtual machine. All in the spirit of event-driven and serverless programming.

Amazon Personalize

Amazon Personalize enables you to increase customer engagement by delivering personalised product and content recommendations, as well as, targeted marketing promotions. Amazon Personalize uses machine learning (ML) to create higher-quality recommendations for your sites and applications. You can get started with no prior ML experience by using simple APIs to easily create advanced personalization features with just a few clicks. All Your data is encrypted to maintain a high level of privacy and security in the cloud and is only used to make recommendations to users.

Amazon Translate

Amazon Translate is a neural machine translation service that provides fast, high-quality, and inexpensive language translation. Neural Machine Translation is a form of language translation automation that uses deep learning models to provide a more accurate and natural-sounding translation than traditional statistical and rule-based translation algorithms. The translation service is trained on a wide variety of content in a variety of use cases and domains to perform well on many types of content. Its asynchronous batching feature allows you to translate a large collection of text or HTML documents with a single API call.

Amazon Fraud Detector

Amazon Fraud Detector is a fully managed service that uses ML and Amazon’s years of fraud detection experience to identify potential fraudulent activities so you can spot more online scams faster. Amazon Fraud Detector automates the time-consuming and costly steps of creating, training, and deploying an ML model for fraud detection, making it easier to use this technology. Amazon Fraud Detector adapts each model it creates to the dataset, so the accuracy of the models is higher than in current universal ML solutions. And because you only pay for what you use, you avoid large upfront costs.

Amazon Extract

Amazon Extract automatically extracts text and data from scanned documents. However, the solution goes beyond a simple OCR and also identifies field content in forms and information stored in tables. This allows Amazon Textract to read virtually any type of document and accurately extract text and data without any manual steps or custom code. After extracting the text and data, you can use Amazon Translate which is a neural machine translation service that provides fast, high-quality, and inexpensive language translation. Its asynchronous batch processing feature allows you to translate a large collection of text or HTML documents with a single API call.

Amazon Kendra

Amazon Kendra is an easy-to-use search engine service based on machine learning. A service dedicated to organisations with a large document database, in which it is difficult to find something and the implemented search methods based on specific solutions do not allow for returning valuable answers.

Is it the right moment to invest in ML and AI?

McKinsey analysts expect the market for AI-related hardware and software solutions to be worth as much as $130 billion in 2025. Besides, according to Werner Vogels, who is chief technology officer and vice president of Amazon in charge of driving technology innovation within the company, now is the best time to develop intelligent applications. It is due to 3 factors.

First, companies collect more and more data, including much from the physical world (e.g. GPS locations). Second, computers are now efficient enough. Third, it is already possible to train even trillions of algorithms simultaneously, which greatly speeds up the learning process. This allowed for more research, which in turn meant that the critical mass in terms of knowledge about artificial intelligence was exceeded. As a result, work on new algorithms and architectures has been significantly accelerated enabling faster and more efficient digital transformation.