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The Future of Data Science and Machine Learning
Data Science and Machine Learning (DSML) are rapidly evolving fields that are shaping the future of technology. According to Gartner, the top trends impacting the future of DSML include Cloud Data Ecosystems, Edge AI, Responsible AI, and Data-Centric AI.
Cloud 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.
Edge AI is growing in demand 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. 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.
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.
Data-Centric AI signifies a shift from model and code-centric approaches to a focus on data to build better AI systems. Solutions like AI-specific data management, synthetic data, and data labeling technologies aim to overcome data challenges.
These trends have far-reaching implications for businesses and society as a whole. As technology continues to advance at an unprecedented pace, it is important for organizations to stay ahead of the curve by adopting these trends and investing in DSML.
The future of DSML is exciting and full of potential. With advancements in Cloud Data Ecosystems, Edge AI, Responsible AI, and Data-Centric AI, we can expect to see significant improvements in technology that will benefit businesses and society as a whole.
