Gartner announces key trends that will shape the future of data science and machine learning

O Gartner, a world leader in research and advice for companies, announces the main trends that will impact the future of Data Science and Machine Learning (Machine Learning) as the sector grows and evolves rapidly to meet the increasing importance of Artificial Intelligence (AI) data and particularly investments in Generative Artificial Intelligence. 

Peter Krensky, analyst and director at Gartner

Second Peter Krensky, analyst and director at Gartner, “As machine learning adoption continues to grow rapidly across industries, DSML (Data Science Machine Learning) is evolving from just focusing on predictive models to a more democratized, dynamic, and data-centric discipline. This is also fueled by the fervor around Generative Artificial Intelligence. Despite the emergence of potential risks, several new capabilities and use cases for data scientists and their organizations are emerging.” 

According to Gartner, the main trends that are shaping the future of Data Science Machine Learning are: 

Trend 1: Cloud Data Ecosystems - You data ecosystems are moving from standalone programs or bundled implementations to complete native solutions stored in A cloud. By 2024, Gartner expects 50% of new Cloud systems implementations to be based on a cohesive data ecosystem rather than manually integrated point solutions. Gartner recommends that companies evaluate data ecosystems based on the ability to solve distributed data challenges as well as access and integrate sources outside of your environment. 

Trend 2: Edge AI – The demand for Edge AI is growing to enable data processing at the point of creation at the edge, helping companies achieve insights in real time, detect new patterns and meet strict data privacy requirements. Edge AI also helps companies improve the development, orchestration, integration and deployment of Artificial Intelligence. 

Gartner predicts that by 2025 more than 55% of all deep neural network data analysis will occur at the point of capture in an edge system, a number that in 2021 was above less than 10%. Companies must identify the applications, AI training and information needed to achieve edge environments near Internet of Things (IoT) devices. 

Trend 3: Responsible Artificial Intelligence – THE Responsible Artificial Intelligence makes technology a positive force, rather than a threat to society and itself. It covers several aspects of the business process and the right ethical choices in technology adoption that companies often address independently, such as commercial and social value, risk, trust, transparency and responsibility. Gartner predicts that the concentration of pre-trained AI models among 1% AI vendors by 2025 will make responsible technology a societal concern. 

The company recommends that companies adopt a risk-proportionate approach to add value to Artificial Intelligence and to take care when applying solutions and models. They must seek guarantees from suppliers to ensure that they are managing their risks and obligations, protecting companies from possible financial losses, legal actions and reputational damage. 

Trend 4: Data Centric AI – Data-centric Artificial Intelligence represents a shift from an approach previously focused on models and codes, creating better systems that rely on this technology. Artificial Intelligence Solutions Data Management, Synthetic Data and Data Labeling aim to solve many challenges, including accessibility, volume, privacy, security, complexity and scopes. 

The use of Generative Artificial Intelligence to create Synthetic Data is an area that is growing rapidly, easing the burden of getting real-world data for business models to use. Machine Learning can be trained efficiently. By 2024, Gartner predicts that 60% of data for Artificial Intelligence will be Synthetic Data to simulate reality, future scenarios and risk technologies, against 1% registered in 2021. 

Trend 5: Accelerated investment in Artificial Intelligence – Investment in Artificial Intelligence will continue to grow through companies that implement the solution, as well as by industries that seek to grow through technologies and businesses based on this technology. By the end of 2026, Gartner predicts that more than US$ 10 billion will be invested in Artificial Intelligence startups that rely on foundation models, which are large technology models trained with a high volume of data. One recent Gartner research with more than 2,500 leading executives found that 45% of these professionals report that the recent hype around ChatGPT led them to increase investments in Artificial Intelligence. Some 70% say their companies are investigating and exploring generative technology, while 19% are in pilot or early production mode.