Gartner, the technology analyst firm, has highlighted the top trends impacting the future of data science and machine learning (ML) as the industry rapidly grows and evolves to meet the increasing significance of data in artificial intelligence (AI), particularly as the focus shifts towards generative AI investments.

Speaking at the Gartner Summit in Sydney, Director Analyst Peter Krensky, said: “As machine learning adoption continues to grow rapidly across industries, DSML is evolving from just focusing on predictive models, toward a more democratized, dynamic and data-centric discipline.

“This is now also fueled by the fervor around generative AI. While potential risks are emerging, so too are the many new capabilities and use cases for data scientists and their organisations.”

The top trends, unsurprisingly, have a focus on AI, but also touch on the wider tech ecosystem, including cloud.

Trend 1. Cloud Data Ecosystems

Data ecosystems are moving from self-contained software or blended deployments to full cloud-native solutions. By 2024, Gartner expects 50% of new system deployments in the cloud will be based on a cohesive cloud data ecosystem rather than on manually integrated point solutions.

Gartner recommends organisations evaluate 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.

Trend 2: Edge AI

Demand for Edge AI — the use of AI techniques which are embedded in Internet of Things (IoT) applications — is growing, according to Gartner.

This technology will enable the processing of data at the point of creation at the edge, helping organisations gain real-time insights, detect new patterns, and meet stringent data privacy requirements.

Edge AI also helps organisations improve the development, orchestration, integration, and deployment of AI.

Gartner predicts that more than 55% 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% in 2021.

Organisations show therefore identify the applications, AI training and inferencing required to move to edge environments near IoT endpoints.

Trend 3: Responsible AI

Responsible AI makes AI a positive force, according to Garnter, rather than a threat to society and to itself.

It covers many aspects of making the right business and ethical choices when adopting AI that organisations often address independently, such as business and societal value, risk, trust, transparency, and accountability.

Gartner predicts the concentrated of pretrained AI models among 1% of AI vendors by 2025 will make responsible AI a societal concern.

The prediction could be sparked by recent trends as the companies and governments come to grips with the ethical and regulatory conundrum introduced by advancing AI models.

Top companies producing AI recently announced their intention to create a Frontier Model Forum which would see competing companies working together with academics and lawmakers to better regulate the growth and deployment of AI.

The EU’s landmark AI Act is set to regulate the industry, introducing copyright stipulations and limitations on the gathering of data for AI models, as well as keeping closer tabs on its use and deployment.

Gartner is recommending organisations adopt a risk-proportional approach to deliver AI value and take caution when applying solutions and models.

Seeking assurance from vendors to ensure they are managing their risk and compliance obligations, protecting organisations from potential financial loss, legal action and reputation damage, is also recommended.

Trend 4: Data-Centric AI

Data-centric AI represents a shift from a model and code-centric approach to being more data focused to build better AI systems.

Solutions such as AI-specific data management, synthetic data and data labeling technologies, aim to solve many data challenges, including accessibility, volume, privacy, security, complexity, and scope.

The use of generative AI to create synthetic data is one area that is rapidly growing, relieving the burden of obtaining real-world data so machine learning models can be trained effectively.

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By 2024, Gartner predicts 60% of data for AI will be synthetic to simulate reality, future scenarios and de-risk AI, up from 1% in 2021.

Trend 5: Accelerated AI Investment

Investment in AI will continue to accelerate by organisations implementing solutions, as well as by industries looking to grow through AI technologies and AI-based businesses.

By the end of 2026, Garter predicts that more than $10 billion will have been invested in AI startups that rely on foundation models — large AI models trained on huge amounts of data.

A recent Gartner poll of more than 2,500 executive leaders found that 45% reported that recent hype around ChatGPT prompted them to increase AI investments.

Seventy percent said their organisation is in investigation and exploration mode with generative AI, while 19% are in pilot or production mode.