Data science development trends in 2022

The development of technologies such as deep learning, natural language processing, computer vision became possible with the emergence of data science as an area of ​​study and practical application. It also allowed machine learning (ML) to emerge.

Data science is a branch of computer science that studies various problems of data analyzing, processing and presenting in digital format. It covers the theoretical and practical applications of ideas, including big data, predictive analytics, and Artificial Intelligence. Up until 10 years ago, data science was considered a niche cross-sectional subject that combined statistics, math, and computing. Now, its availability is increasing, and its importance for business is understood. There are many ways to learn it, including online courses, in-house training, etc. Let’s consider some of the data science development trends in 2022 and beyond.

Small data and TinyML

Big data is often referred to as the growth in digital data that is generated, collected and analyzed by humans on a daily basis. Machine learning algorithms for processing large data amounts can also be quite large. Thus, GPT-3 is the largest and most complex system capable of simulating human language. It consists of about 175 billion parameters.

Machine learning can add value to cloud systems with unlimited bandwidth. That’s why the concept of «Small Data» arose and makes it possible to simplify the quick cognitive analysis of the most important data in situations where time, bandwidth, energy costs are essential. For example, self-driving cars can’t count on the ability to send and receive data from a centralized cloud server trying to avoid an accident.

TinyML refers to machine learning algorithms that take up as little space as possible and can run on low-power hardware near the scene of the action. In 2022, the number of its appearances in embedded systems (household appliances, cars, industrial equipment, agricultural equipment) will increase and make them smarter and more functional.

Data-driven customer service

Customer data is the main source of companies to improve the quality of customer service: product or service upgrading, the e-commerce process simplifying, a more user-friendly interface creating, waiting times reducing, etc.

The interaction between the client and the company is becoming more digital. Any action can be measured and analyzed for a better understanding of how processes can be improved, as well as personalized goods and services offered to the client. The pandemic has sparked a wave of investment and innovation in online commerce technology. Companies sought to completely replace physical shopping trips. Finding new methods and strategies to use data to improve customer service will remain one of the top trends in 2022.

Deepfake, generative AI, synthetic data

Deepfake is a realistic substitution of photo, video, audio content based on generative AI. This technology is widespread in the arts and entertainment. Deepfakes are expected to spread to other industries and use cases in 2022. For example, creating synthetic data for training machine learning algorithms. By creating synthetic faces of non-existent people in order to train face recognition algorithms. This will help to avoid problems with confidentiality and real people faces usage. Also, the application of this technology is possible in medicine (for example, for training systems for recognizing signs of rare cancer types); for converting a language into an image (for example, creating a building image based on a verbal description of its type).


Digital transformation key elements are Artificial Intelligence (AI), Internet of Things (IoT), cloud computing, superfast networks (5G). Each of these technologies exists in isolation, but they are all interconnected, allowing to do more. For example, AI allows IoT devices to act intelligently, interact with other technologies with minimal human intervention. It contributes to automation and the creation of smart homes, factories and even cities. 5G and other superfast networks allow to transfer data at higher speeds. Moreover they will allow to become commonplace with new types of data transfer. AI algorithms play a key role in routing traffic to ensure optimal transfer rates, automating control of the cloud data center environment. In 2022, the development of these technologies and their interaction with each other will be observed.


AutoML (Automated Machine Learning) helps to democratize data science. Data cleansкаing and preparing is a time-consuming routine for a data scientist. AutoML assumes the automation of such tasks. The goal of this technology is to create tools and platforms that anyone can use. Thus, with the help of user-friendly interfaces, each user can apply machine learning to solve problems and validate ideas. It is predicted that in 2022 AutoML will actively evolve to become an everyday reality.

Cloud Computing Trends 2022

With the outbreak of the pandemic, the world has changed in many ways, work has become more virtual, enterprises have adapted to new conditions and focused on the provision of digital services. As a result, cloud computing has grown significantly over the past 2 years and will continue to grow in 2022.

The emphasis is likely to shift from deploying cloud-based tools and platforms to more holistic strategies focused on cloud migration across the enterprise. As always, empowering the remote and hybrid workforce will be a priority.

New use cases

In 2020, global cloud spending was $313 billion. Gartner predicts spending in 2022 will amount to $482 billion. The core of the delivery process for any digital service (social media, streaming, connected car, IoT, etc.) is cloud computing infrastructure. Ultra-fast networks (5G and WI-Fi 6E), in addition to transferring even more data from the cloud, will transfer new types of data. The advent of cloud-based virtual reality (VR / AR) will drive headsets down in size and cost. Cloud technologies make other technologies faster, easier and more accessible to the user. This is a key factor in the services transition to cloud platforms.

Environmental safety is becoming a driving factor in the development of cloud services

Significant climate changes are now taking place. Every responsible company must take part in solving environmental problems. Often in the field of technology, this comes down to reducing energy consumption, what is associated with high requirements for digital storage, providing users with round-the-clock services, and more powerful processors. In 2022, tech giants plan to invest in solving such problems.

Hybrid cloud blurs the line between public and private clouds

Traditionally, companies moving to the cloud have had 2 options: a public or private cloud solution. The latter is characterized by the possibility of more flexible customization, the organization has practically its own separate cloud, information from which should not go beyond its scope. In some cases, such a decision takes place. However, major cloud providers Microsoft, IBM, Amazon are developing and promoting hybrid models using the best of the 2 options. In this case, data frequently used by users (for example, by customers) can be stored on public servers, which can be accessed using applications, dashboards. Private servers can store sensitive and sensitive information. Access to this data can be completely controlled and processed through a dedicated application.

Now many companies are trying to master cloud computing, which has become another reason for the popularity of hybrid cloud. Companies understand the full benefits of the cloud and are looking for additional use cases. For many companies, this has led to a «multi-cloud environment» using a range of services from multiple providers. The hybrid cloud option can simplify this process by optimizing the user experience.

Artificial Intelligence in Cloud Computing

Cloud computing plays a key role in the delivery of Artificial Intelligence services. The need for machine learning platforms for large computing power and data throughput for training and processing can be covered by cloud data centers. Every day we come across AI services (Google search, Instagram filters etc.) that are in the cloud. The technology that distributes traffic from the data processing and storage center to user devices is based on Machine Learning. The development of AI and cloud computing is connected and will become more relevant in the coming years.

Serverless Cloud

Now, a new concept is gaining momentum in the market – a serverless cloud. The main suppliers are companies such as Microsoft (Azure Function), IBM Cloud Function, Amazon (AWS Lambda). This concept is that companies are not tied to renting servers and paying for a certain amount of storage. You pay as you go, and the infrastructure scales as the application demands it. The server certainly exists. However, another layer is added between the user and the platform, which allows the user not to touch the settings and technical details.

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