Gartner, Inc. today highlighted key trends impacting the future of data science and machine learning (DSML), as the industry grows and rapidly evolves to meet the importance growing data in artificial intelligence (AI), particularly as focus shifts to Generative AI investments.
Speaking at Gartner Data and Analytics Summit in Sydney today, Pierre Krenski, director analyst at Gartner said: “As the adoption of machine learning continues to grow rapidly across industries, DSML is evolving from a simple focus on predictive models to a more democratized, dynamic and business-centric discipline. data. This trend is now also fueled by the fervor around generative AI. While potential risks arise, so do the many new capabilities and use cases for data scientists and their organizations.
According to Gartner, key trends shaping the future of DSML include:
Trend 1: Cloud Data Ecosystems
Data ecosystems are moving from standalone software or mixed deployments to complete cloud-native solutions. By 2024, Gartner predicts that 50% of new system deployments cloud will be based on a cohesive cloud data ecosystem rather than manually integrated point solutions.
Gartner recommends that organizations evaluate data ecosystems based on their ability to solve distributed data problems, as well as access and integrate data sources outside of their immediate environment.
Trend 2: Advanced AI
Request Cutting-edge AI is expanding to enable data processing at the point of creation at the edge, helping organizations gain real-time insights, detect new patterns, and meet stringent data privacy requirements. Edge AI also helps organizations improve AI development, orchestration, integration and deployment.
Gartner predicts that more than 55% of all data analysis by deep neural networks will take place at the point of capture in an edge system by 2025, up from less than 10% in 2021. Organizations must identify applications, the AI training and inference needed to migrate to edge environments near IoT endpoints.
Trend 3: responsible AI
Responsible AI makes AI a positive force rather than a threat to society and itself. It covers many aspects of making appropriate business and ethical decisions when adopting AI, which organizations often address independently, such as business and societal value, risk, trust, transparency and the responsibility. Gartner predicts that the concentration of pre-trained AI models among 1% of AI vendors by 2025 will make responsible AI a societal concern.
Gartner recommends that organizations adopt a approach proportional to risk to bring value to AI and exercise caution when applying solutions and models. Ask suppliers to ensure they are managing their risks and compliance obligations well, protecting organizations against potential financial losses, lawsuits and reputational damage.
Trend 4: Data-centric AI
Data-centric AI represents a shift from a model- and code-centric approach to a more data-driven approach to building better AI systems. Solutions such as AI-specific data management, synthetic data, and data labeling technologies aim to solve many data-related problems, including accessibility, volume, privacy, security, complexity and scope.
The use of Generative AI Creating synthetic data is a rapidly growing field, easing the burden of obtaining real-world data so that machine learning models can be trained efficiently. By 2024, Gartner predicts that 60% of data for AI will be synthetic to simulate reality, future scenarios and reduce AI risks, up from 1% in 2021.
Trend 5: Accelerated investment in AI
Investments in AI will continue to accelerate between now and organizations implementing solutions, as well as by industries looking to grow with AI technologies and AI-enabled businesses. By the end of 2026, Gartner predicts that more than $10 billion will have been invested in AI startups that rely on core models – large AI models trained on huge amounts of data.
A recent Gartner survey of more than 2,500 executives found that 45% said the recent hype around ChatGPT had inspired them to increase their investments in AI. Seventy percent said their organization is in investigation and exploration mode with generative AI, while 19% are in pilot or production mode.
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