How to conduct migrartion to cloud step by step guide

AWS AI services and ML solutions: let’s explore them! 

date: 30 March 2021
reading time: 9 min

Invent is an annual event where AWS executives present the strategy of the cloud business development. At the conference held in December 2019, the service provider had convinced customers and partners that the next stage of cloud development would be artificial intelligence, used to build and manage services and helpful in creating other solutions using machine learning models.

It has therefore become a priority to expand the Amazon Web Services technology with this type of mechanism, as well as to create new services facilitating the implementation of artificial intelligence in the IT environment of customers.

Keen to know more? Before jumping into the extensive portfolio of available solutions, let us elaborate more on AI, Machine Learning and Deep Learning technology.

Understanding the power of AWS AI services

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. Behavioural algorithms, virtual agents, or autonomously powered, self-propelled vehicles with predictive capabilities – these are but a few of examples of the use of artificial intelligence today.

The role of Machine Learning in AWS

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.

Advanced Deep Learning technologies

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 process 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, cybersecurity services and image recognition.

Read more about the different applications of AI in business:

Things Artificial Intelligence can do for your business

The power of Artificial Intelligence in healthcare – best examples

Artificial Intelligence in FinTech: maximise benefits by innovating responsibly

Predicting stock prices and market shifts using AI

Selection of AI-based services offered by Amazon

With all this theory in mind, let’s now look at the selection of AI-based services offered by Amazon.

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 real interaction.

It is widely used in the implementation of chatbots that improve communication and customer service, it also allows 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, a training task that takes ten hours on a single computer can be run on ten computers and be 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 to build quite a sizeable database and implement certain algorithms. Today, 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 personalisation 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 affordable 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 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.

Amazon Kendra

Amazon Kendra is an easy-to-use search engine service based on machine learning. It is 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.

Real-world applications of AWS AI and ML service

Amazon Web ML and AI services can be applied to various real-world scenarios.

Some of examples include:

  1. Recommendation Systems using Amazon Personalize. This technology is widely used in e-commerce platforms, streaming services and content delivery systems to provide personalised recommendations to users based on their preferences and behaviours.

  2. Natural Language Processing (NLP) using Amazon Comprehend to extract insights and relationships from text data. NLP techniques can be applied in sentiment analysis, content categorisation, entity recognition and language detection for applications such as social media monitoring and customer feedback analysis.

  3. Chatbots and Virtual Assistants: Amazon Lex and Amazon Polly can be used to build chatbots and virtual assistants. These services find applications in customer support, interactive applications and accessibility tools.

  4. Image and Video Analysis using Amazon Rekognition. This service is used in various industries including security, content moderation, retail and entertainment.

  5. Anomaly Detection: Amazon Lookout for Metrics uses machine learning to automatically detect anomalies in business metrics and time-series data. This is particularly useful for detecting anomalies in operational data, helping businesses identify issues and take corrective actions.

  6. Predictive Analytics with Amazon Forecast, used in demand forecasting, inventory planning, resource allocation, and other applications that require predicting future trends based on historical data.

  7. Healthcare Applications: Amazon Comprehend Medical can extract medical information from unstructured text, making it useful for medical research, coding medical records, and clinical decision support.

  8. Financial Services: Amazon Fraud Detector uses machine learning to identify potentially fraudulent activities in real time. This is valuable for financial institutions and e-commerce platforms to prevent fraudulent transactions.

Is it the right moment to invest in AWS ML and AI-based solutions?

McKinsey analysts expect that in 2025 the market for AI-related hardware and software solutions will be worth as much as $130 billion.

According to Werner Vogels, 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 allows for more research, which in turn means that the critical mass in terms of knowledge about artificial intelligence is exceeded.

As a result, work on new algorithms and architectures has been significantly accelerated, enabling faster and more efficient digital transformation.

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