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The Future of Data Science and Machine Learning According to Gartner Research

Data science and machine learning (DSML) are rapidly evolving fields that are having an increasing impact on artificial intelligence (AI). As machine learning adoption grows across industries, DSML is shifting from just focusing on predictive models to becoming a more democratized, dynamic, and data-centric discipline. This evolution is fueled by the excitement around generative AI. While risks are emerging, many new capabilities and use cases for data scientists are also arising, as the latest research shows.

Cloud Data Ecosystems

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

Organizations should evaluate data ecosystems based on their ability to resolve distributed data challenges and integrate with data sources outside their immediate environment. The move to cloud data ecosystems will provide more flexibility and accessibility.

The Rise of Edge AI

Demand for edge AI is escalating to enable real-time data processing at the point of creation. This helps organizations gain instant insights, identify new patterns, and meet strict data privacy requirements. Edge AI also improves the development, orchestration, integration, and deployment of AI systems.

Gartner forecasts over 55% of deep neural network analysis will happen at the point of data capture by 2025, up from under 10% in 2021. Organizations should determine the applications, training, and inferencing required to shift to edge environments near IoT endpoints.

The Need for Responsible AI

Responsible AI aims to make AI a positive societal force rather than a threat. It encompasses making the right ethical choices when adopting AI related to business value, risk, trust, transparency, and accountability.

Gartner predicts the concentration of pre-trained AI models among 1% of vendors by 2025 will make responsible AI a public concern. Organizations should take a risk-proportional approach to deliver AI value cautiously. They should ensure vendors are managing obligations to avoid potential damages.

The Shift to Data-Centric AI

Data-centric AI moves from a model and code-heavy approach to prioritizing data to construct superior AI systems. Solutions like AI data management, synthetic data, and data labeling aim to address data challenges like volume, privacy, security, complexity, and accessibility.

Generative AI's ability to create synthetic data is rapidly growing, reducing the need for real-world data to effectively train machine learning models. Gartner forecasts 60% of data for AI will be synthetic by 2024, up from 1% in 2021.

Accelerating AI Investment

Investment in AI will continue to accelerate as organizations implement solutions and industries seek to expand through AI technologies and businesses.

Gartner predicts over $10 billion will be invested in AI startups relying on foundation models by the end of 2026. Recent hype around ChatGPT has spurred 45% of executives to increase AI investments. While most organizations are still exploring generative AI, 19% are piloting or deploying it.

The future of DSML looks bright yet complex as the field grows more sophisticated and influential. Organizations should stay informed on the latest trends and judiciously evaluate new capabilities to make the most of emerging innovations.

Book a Demo with Sapien for Superior Data Labeling

As discussed in this blog post, quality data is crucial for developing effective AI systems. However, labeling training data can be tedious and time-consuming. This is where Sapien's data labeling services come in.

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