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TOP 11 DATA SCIENCE TRENDS IN 2022

Excerpt: Big data has become a household name for businesses nowadays. It has become an integral part of all sorts of businesses, especially for those enterprises that leverage data to gather insights. Data Science has become a meeting point for science and AI in this day and age. Even after the onset of the pandemic, the field has seen tremendous growth.

Here are the top data science trends in 2022 that will positively impact your business.

  1. The boom in Cloud Migration

Migration to the public cloud or the expansion of the private cloud was the top IT spending driver in 2020. It is projected that enterprises will start preparing for application migration with the help of the process of containerizing their on-premise applications. This can be an outcome of chip shortages, cost considerations, and the dire requirement for scalability.

Companies are projected to migrate their transaction process that takes place online, data warehouses, ETL, analytics, and other related things to the cloud. There are a lot of businesses that have already built hybrid or multi-cloud executions that have their sole consideration on porting of the data processing as well as analytics. By carrying out this procedure, they will have their sole concentration on moving from one cloud service provider to another without having to worry about issues like lock-in periods and leveraging specific point solutions.

2. Growth of Predictive Analysis

One of the best cases of correct use of data analysis is Netflix. It has been able to influence 80 of its users with the help of correct data insights. Predictive analysis, for starters, is all about future predictions and forecasts that assist in the growth of an organization.

An organization might consider how they wish to strategy and alter their goals as a result of data-driven insights that is an outcome of predictive analytics. The worldwide predictive analytics market is projected to reach 21.5 billion USD by 2025, with an expansion rate of 24.5% CAGR. The huge increase projected here can be attributed to the fact that the widespread acceptance of digital transformation across a variety of enterprises.

3. Tiny ML

Tiny ML can be described as a type of ML that shrinks deep learning networks to let them fit into any hardware. The versatility, cost-effectiveness, and tiny form factor make it one of the most notable trends when it comes to the field of data science.

Several different applications can be built by taking the help of Tiny ML. It embeds AI on different small pieces of hardware and solves the issue related to embedded AI: power and space. On-device machine learning gets applications in a lot of different types of settings.

BeF construction automation or drug research and testing leads to faster iteration cycles, gathers feedback, and allows the user to explore more. Tiny ML is commonly used in audio analytics, detection, and speech-human-machine interfaces. Although audio analytics assists in the care of children and the elderly, it also helps in the monitoring and safety of the equipment. TinyML may also be utilized for vision, motion, gesture recognition, and audio. According to McKinsey, as of now, there are over 250 billion active embedded devices in the globe. TinyML has the potential to eliminate the disparity that exists between edge hardware and device intelligence.

TinyML also offers the potential to incorporate AI and computing in a relatively cheaper, scalable, and predictable manner when newer human-machine interfaces emerge. As a result, tiny XML device shipments are projected to reach 2.5 billion by 2030, up from 15 million in 2020.

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4. Auto ML

AutoML, or Automated Machine Learning, can go down as one of the most recent innovations that are furthering the goal of the democratization of data science. A significant portion of a data scientist’s work and time is spent on data purification and preparation. Both of these processes are repetitive and time-consuming operations. AutoML guarantees that these processes run in an automated mode by generating models, algorithms, and neural networks.

AutoML is a state-of-the-art technique for applying machine learning models to real-world problems with the help of automation. Data scientists benefit from AutoML frameworks in data visualization, model intelligibility, and deployment. Its key innovation includes hyperparameters search, which is used for pretreatment components, model type selection, and hyperparameter optimization.

5. Cloud-Based AI and Data solutions

Cloud-based solutions will become increasingly popular with time. Data is already being generated in a large volume. The problem lies with gathering, labelling, cleaning, organizing, structuring, and analyzing this huge chunk of data in one area. The solution in a case like this will be a cloud-based platform.

The next several years will be a crucial time for the battle for minds, arms, and budgets among Cloud Computing powerhouses in the Data Science and Machine Learning business. Although AWS looks and is projected to be in a stronger position than its competitors, GCP’s challenges may be a fascinating aspect of market restructuring in the coming years. At the same time, Microsoft Azure might also be retaining its lead in North America.

6. Augmented Customer Interfaces

Shortly, an AI agent in the form of an interface may be available to assist people with chores like shopping. You may also be able to purchase your items in VR, hear about them through audio, or also through an enhanced consumer interface. Augmenting consumer interfaces may take a lot of different forms, including augmented reality on mobile devices and communication interfaces such as a Brain-Computer Interface (BCI).

These technologies have immediate ramifications for how we carry out the purchasing process. Even your Zoom sessions might be rendered obsolete with the advent of these enhanced consumer interfaces. This enhanced consumer interface will also be including the metaverse that Facebook, Microsoft, and other corporations are on their way to developing. IoT, BCI, AI, VR, AR speakers, AI agents, and other related technologies will enhance consumer interfaces. All of this will culminate in a new paradigm with artificial intelligence as the mediator.

7. Better Data Regulation

Optimization of big data needs to be an afterthought. With data regulating every element of AI, predictive analytics, and so on, businesses must use their data with caution and utmost meticulousness. Data privacy is no longer a buzzword in this day and age. If we go by the Cisco Consumer Privacy Survey 2019, 97% of organizations acknowledged that investing in data privacy provides many different benefits, such as competitive advantage and investor attractiveness.

With AI permeating industries such as healthcare, critical EMRs and organizations must ensure that patient data is not jeopardized. Data privacy by design will contribute to a more secure method for gathering and processing user data while the machine will learn to do it independently. What the user does and how they move and grow on the cloud should all be scrutinized from a legislative and regulatory standpoint.

The rate at which data science and related tools are growing is astounding. Yet, very few efforts govern data privacy or assure safety and purity regarding consumer data. AI systems might also result in massive falls if there is an absence of a stringent regulating authority to monitor their upkeep.

8. AI as a Service (AIaaS)

It refers to companies responsible for providing out-of-the-box AI solutions, helping clients install and grow AI techniques at a lower and affordable cost. For example, OpenAI recently announced that it would make GPT-3, its transformer language model, available to the public as an API. AIaaS is one of the most recent developments, which comes with cutting-edge models offered as a service.

This technology’s future sees its growth with the help of well-defined and self-contained functions. A manufacturing company, for instance, will employ one provider to construct a chatbot for internal communication, and another will be there to anticipate inventories. Moreover, complex algorithms that give specialized answers may be generated per the requirements, thanks to a rise in the number of domain expert AI models.

9. Cloud-native solutions will become a must-have

The term “cloud-native” is commonly used to characterize container-based settings. They are used to create applications that employ services bundled in containers. Containers get their deployment in microservices on elastic infrastructure and are controlled with the help of agile DevOps methods and continuous delivery workflows. A cloud-native infrastructure consists of software and hardware that can be used to execute the apps efficiently.

Operating systems, data centers, deployment pipelines, and a slew of apps would also become a part of this infrastructure. Given the fact that there is widespread acceptance of digital transformation, a lot of firms now operate very well in a cloud-based environment. Building on-premise infrastructure is quite expensive for an organization, which is why cloud-based is the preferred alternative these days. It also entails using cloud-native analytics systems to generate thorough cloud analysis.

10. Training Data Complexities

Despite all of the narratives that have been going around about data being the new oil and how crucial it is for businesses, the majority of the data generated goes completely unused. It is also known as black data since it is generally gathered, processed, and retained solely to fulfill compliance purposes. Furthermore, because 80-90% of the data generated by modern organizations remain completely unstructured, analyzing them becomes much more challenging.

Massive volumes of training data are required to construct viable machine-learning models. Unfortunately, this is one of the primary impediments that need the help of supervised or unsupervised learning applications. In addition, there are several situations where a substantial data repository is not accessible to the users, which can significantly hinder data science initiatives.

11. Human Jobs will remain safe.

People thought that artificial intelligence (AI) would invade and take their employment. This is completely untrue, as AI has worked as a catalyst in ensuring that human occupations are better optimized than it has ever been in the past. At the same time, AI-powered technologies help you get things done faster and with fewer mistakes; your jobs aren’t going away anytime soon in the near or far future.

Conclusion

Data Science consists of practical and theoretical applications for ideas relating to technologies like big data, AI, and predictive analytics. The market is only projected to grow bigger in the coming years, and many different organizations are embracing them with open hearts. This is why it is high time for you to utilize these technologies to improve your business.

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