Cloud Hosting

According to analyst group Gartner, Cloud Data Ecosystems and Edge AI are two of the top trends impacting the future of data science and machine learning.

Speaking at the Gartner Data & Analytics Summit in Sydney in August 2023, Peter Krensky, director analyst at Gartner, said: "As machine learning adoption continues to grow rapidly across industries, data science and machine learningor DSMLis 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.

In addition to Cloud Data Ecosystems and Edge AI, Gartner cited three other key trends: Responsible AI, Data-Centric AI, and Accelerated AI Investment.

The Summit heard that 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 manually integrated point solutions.

Demand for Edge AI is growing 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 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.

On Responsible AI, Gartner said this trend made AI a positive force rather than a threat to society and itself. Gartner predicts the concentration of pre-trained AI models among 1% of AI vendors by 2025 will make responsible AI a societal concern.

Data-centric AI represents a shift from a model and code-centric approach to being more data-focused to build better AI systems.

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 de-risk AI, up from 1% in 2021.

Gartner also forecasts that investment in AI will continue to accelerate by organizations implementing solutions and industries looking to grow through AI technologies and AI-based businesses.

By the end of 2026, Gartner predicts that more than USD10 billion will have been invested in AI startups that rely on foundation models large AI models trained on vast amounts of data.

Image credit: iStockphoto/NicoElNino

View post:

Data Science More Democratized and Dynamic: Gartner - CDOTrends