Gartner identifies top trends shaping the future of DSML
Gartner has identified the top trends impacting the future of data science and machine learning (DSML) as the industry evolves to meet the increasing significance of data in AI, particularly as the focus shifts to generative AI.
Over two days (July 31 - August 1), Gartner analysts presented the latest research and advice for data and analytics leaders at the Gartner Data & Analytics Summit in Sydney.
Speaking at the Summit, Peter Krensky, Director Analyst at Gartner, says: "As machine learning adoption continues to grow rapidly across industries, DSML is evolving from just focusing on predictive models toward a more democratised, dynamic and data-centric discipline."
"This is now also fuelled by the fervour around generative AI. While potential risks are emerging, so too are the many new capabilities and use cases for data scientists and their organisations."
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 complete 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 manually integrated point solutions.
Gartner recommends that organisations evaluate data ecosystems based on their ability to resolve distributed data challenges and access and integrate with data sources outside their immediate environment.
Trend 2: Edge AI
Demand for edge AI is growing to enable data processing at the point of creation, helping organisations gain real-time insights, detect new patterns and meet stringent data privacy requirements. Edge AI also helps organisations improve AI development, orchestration, integration and deployment.
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.
Gartner suggests that organisations identify the applications, AI training and inferencing required to move to edge environments near IoT endpoints.
Trend 3: Responsible AI
Responsible AI covers aspects of making the right business and ethical choices when adopting AI.
Gartner predicts the concentration of pre-trained AI models among 1% of AI vendors by 2025 will make responsible AI a societal concern.
Gartner recommends organisations adopt a risk-proportional approach to deliver AI value and take caution when applying solutions and models. Furthermore, seeking assurances from vendors to confirm they manage their risk and compliance obligations protects organisations 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 labelling 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 and future scenarios and derisk AI, up from 1% in 2021.
Trend 5: Accelerated AI Investment
Investment in AI will continue to accelerate by organisations implementing solutions and industries looking to grow through AI technologies and AI-based businesses.
By the end of 2026, Gartner predicts that more than USD $10 billion will have been invested in AI startups that rely on foundation models, large AI models trained on large amounts of data.
A recent Gartner poll of over 2,500 executive leaders found that 45% reported that recent hype around ChatGPT prompted them to increase AI investments. 70% said their organisation is in investigation and exploration mode with generative AI, while 19% are in pilot or production mode.
