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Gartner identifies top trends shaping the future of data science and machine learning

Gartner, Inc. today highlighted the top trends impacting the future of data science and machine learning (DSML) 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.

Peter Krensky, Director Analyst at Gartner 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 organizations.”

According to Gartner, the top trends shaping the future of DSML include:

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 organizations 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 is growing to enable the processing of data at the point of creation at the edge, helping organizations to gain real-time insights, detect new patterns and meet stringent data privacy requirements. Edge AI also helps organizations 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. Organizations should 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, 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 organizations often address independently, such as business and societal value, risk, trust, transparency and accountability. Gartner predicts the concentration of pretrained AI models among 1% of AI vendors by 2025 will make responsible AI a societal concern.

Gartner recommends organizations adopt a risk-proportional approach to deliver AI value and take caution when applying solutions and models. Seek assurances from vendors to ensure they are managing their risk and compliance obligations, protecting organizations from potential financial loss, legal action and reputational damage.

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

Trend 5: Accelerated AI Investment
Investment in AI will continue to accelerate by organizations implementing solutions, as well as by industries looking to grow through AI technologies and AI-based businesses. By the end of 2026, Gartner 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.

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