In a new report, Gartner has identified five trends that are impacting the future of data science and machine learning (DSML).
These trends take into account the growing significance of data in artificial intelligence (AI), particularly generative AI, as the technology continues to explode across nearly every industry.
Peter Krensky, director analyst at Gartner, speaking at Gartner Data & Analytics Summit in Sydney, explained that DSML “is evolving from just focusing on predictive models, toward a more democratized, dynamic and data centric discipline.” He added that while risks with generative AI are emerging, so are the many capabilities and use cases for data scientists and their organizations.
The five trends identified by Gartner are as follows:

Cloud data ecosystems are moving from self-contained software or blended deployments to full cloud-native solutions. Gartner advises organizations to evaluate their data ecosystems based on their ability to resolve distributed data challenges, as well as to access and integrate with data sources outside of their immediate environment.
Edge AI, which enables organizations to process data at the point of creation at the edge, is growing in demand. Gartner predicts that more than 55 per cent of all data analysis by deep neural networks will occur at the point of capture in an edge system by 2025, up from less than 10 per cent in 2021. Organizations should identify the applications, AI training and inferencing required to move to edge environments near IoT endpoints. 
Responsible AI – Gartner predicts the concentration of pretrained AI models among one per cent of AI vendors by 2025 will make responsible AI a societal concern. Gartner recommends that organizations adopt a risk-proportional approach when applying AI solutions and models, and seek assurances from vendors to ensure they are compliant with regulations and are managing risks.
Data-centric AI will see solutions such as AI-specific data management, synthetic data, and data labeling technologies evolve to solve data challenges including accessibility, volume, privacy, and more. By 2024, Gartner predicts 60 per cent of data for AI will be synthetic, to simulate reality and future scenarios and de-risk AI, up from 1 per cent in 2021.
Investment in AI will continue to accelerate, with organizations implementing new solutions, as well as in industries looking to grow through AI technologies and AI-based businesses. By the end of 2026, Gartner predicts that more than US$10 billion will have been invested in AI startups that rely on foundation models.
The post Top trends impacting the future of data science and machine learning: Gartner first appeared on IT World Canada.