Skip to main content

Gartner: Top Trends Shaping Future of Data Science and Machine Learning

Gartner 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.

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. 75% said their organization is in investigation and exploration mode with generative AI, while 19% are in pilot or production mode.

Hot Topics

The Latest

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

Image
Broadcom

From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...

Today, organizations are generating and processing more data than ever before. From training AI models to running complex analytics, massive datasets have become the backbone of innovation. However, as businesses embrace the cloud for its scalability and flexibility, a new challenge arises: managing the soaring costs of storing and processing this data ...

Despite the frustrations, every engineer we spoke with ultimately affirmed the value and power of OpenTelemetry. The "sucks" moments are often the flip side of its greatest strengths ... Part 2 of this blog covers the powerful advantages and breakthroughs — the "OTel Rocks" moments ...

OpenTelemetry (OTel) arrived with a grand promise: a unified, vendor-neutral standard for observability data (traces, metrics, logs) that would free engineers from vendor lock-in and provide deeper insights into complex systems ... No powerful technology comes without its challenges, and OpenTelemetry is no exception. The engineers we spoke with were frank about the friction points they've encountered ...

Enterprises are turning to AI-powered software platforms to make IT management more intelligent and ensure their systems and technology meet business needs for efficiency, lowers costs and innovation, according to new research from Information Services Group ...

The power of Kubernetes lies in its ability to orchestrate containerized applications with unparalleled efficiency. Yet, this power comes at a cost: the dynamic, distributed, and ephemeral nature of its architecture creates a monitoring challenge akin to tracking a constantly shifting, interconnected network of fleeting entities ... Due to the dynamic and complex nature of Kubernetes, monitoring poses a substantial challenge for DevOps and platform engineers. Here are the primary obstacles ...

The perception of IT has undergone a remarkable transformation in recent years. What was once viewed primarily as a cost center has transformed into a pivotal force driving business innovation and market leadership ... As someone who has witnessed and helped drive this evolution, it's become clear to me that the most successful organizations share a common thread: they've mastered the art of leveraging IT advancements to achieve measurable business outcomes ...

More than half (51%) of companies are already leveraging AI agents, according to the PagerDuty Agentic AI Survey. Agentic AI adoption is poised to accelerate faster than generative AI (GenAI) while reshaping automation and decision-making across industries ...

Image
Pagerduty

 

Gartner: Top Trends Shaping Future of Data Science and Machine Learning

Gartner 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.

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. 75% said their organization is in investigation and exploration mode with generative AI, while 19% are in pilot or production mode.

Hot Topics

The Latest

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

Image
Broadcom

From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...

Today, organizations are generating and processing more data than ever before. From training AI models to running complex analytics, massive datasets have become the backbone of innovation. However, as businesses embrace the cloud for its scalability and flexibility, a new challenge arises: managing the soaring costs of storing and processing this data ...

Despite the frustrations, every engineer we spoke with ultimately affirmed the value and power of OpenTelemetry. The "sucks" moments are often the flip side of its greatest strengths ... Part 2 of this blog covers the powerful advantages and breakthroughs — the "OTel Rocks" moments ...

OpenTelemetry (OTel) arrived with a grand promise: a unified, vendor-neutral standard for observability data (traces, metrics, logs) that would free engineers from vendor lock-in and provide deeper insights into complex systems ... No powerful technology comes without its challenges, and OpenTelemetry is no exception. The engineers we spoke with were frank about the friction points they've encountered ...

Enterprises are turning to AI-powered software platforms to make IT management more intelligent and ensure their systems and technology meet business needs for efficiency, lowers costs and innovation, according to new research from Information Services Group ...

The power of Kubernetes lies in its ability to orchestrate containerized applications with unparalleled efficiency. Yet, this power comes at a cost: the dynamic, distributed, and ephemeral nature of its architecture creates a monitoring challenge akin to tracking a constantly shifting, interconnected network of fleeting entities ... Due to the dynamic and complex nature of Kubernetes, monitoring poses a substantial challenge for DevOps and platform engineers. Here are the primary obstacles ...

The perception of IT has undergone a remarkable transformation in recent years. What was once viewed primarily as a cost center has transformed into a pivotal force driving business innovation and market leadership ... As someone who has witnessed and helped drive this evolution, it's become clear to me that the most successful organizations share a common thread: they've mastered the art of leveraging IT advancements to achieve measurable business outcomes ...

More than half (51%) of companies are already leveraging AI agents, according to the PagerDuty Agentic AI Survey. Agentic AI adoption is poised to accelerate faster than generative AI (GenAI) while reshaping automation and decision-making across industries ...

Image
Pagerduty