The Future of Data Science and Machine Learning
Gartner points to trends such as data-centric, perimeter, and accountable intelligence.
The world is turning to artificial intelligence (AI), with the growing popularity of generative AI. And this marks the future of the data science and machine learning industry, known as DSML.
“DSML is evolving from a simple focus on predictive modelling to a more democratised, dynamic, and data-centric discipline,” says Peter Krensky, principal analyst at Gartner, who believes that, “while the potential risks are emerging, so are the many new capabilities and use cases for data scientists and their organisations”.
Among the most notable trends in this area, Gartner highlights the popularisation of cloud data ecosystems and accelerated investment in AI.
The consultancy believes that next year half of all new cloud system implementations will be based on a cloud data ecosystem rather than manually integrated point solutions.
Slightly later, by the end of 2026, more than $10 billion should be invested in startups based on big data-trained AI models.
Other trends include data-centric AI, perimeter AI for data processing at the point of creation, and accountable AI.
For example, by 2024, 60% of data for AI is expected to be synthetic, helping to simulate reality, future scenarios and eliminate risks. By 2025, more than 55% of data analysis by deep neural networks should happen at the point of capture in edge systems.
