How will AI & Machine Learning Redefine Data Science in 2025?
In an era, where data is more valuable than a currency, fields like Data Science, Machine Learning, and AI are growing at a rapid pace. Be it customer experiences, preferences, advanced research or operations, personalized marketing, and supply chain optimization, data science has taken the world by storm and is continuing to evolve.
As 2025 is approaching, three major trends emerging in data science are – AI expansion, demand for transparent AI systems (XAI), and growing accessibility of data science through AutoML and low-code platforms. In this article, we will delve into the world of data science figuring out how AI and Machine Learning are set to redefine data science in 2025.
Without taking much time, let us dive right in!
Automated Machine Learning (AutoML)
Recent reports from Gartner state that the global AutoML market is projected to grow from $346 million to $14 billion and above by 2026. AutoML platforms are making it easier for non-experts to develop and deploy machine learning models.
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AutoML tools allow users to focus on higher-level business problems by automating complex issues like feature selection, model training, and hyperparameter tuning. AutoML is good for SMEs and small businesses that lack extensive data research expertise. By automating the end-to-end process of applying ML to real problems, AutoML significantly reduces the time and expertise needed to develop ML models.
Rise of XAI
Some of you might be reading this term for the first term, right? Let me tell you what Explainable AI is? It refers to AI systems designed to present their inner workings in a comprehensible manner to humans. Explainable AI is important in industrial sectors like healthcare, legal, and finance.
A McKinsey survey concluded that 57% of organizations prefer to have XAI in their AI adoption strategies. By 2025, businesses can expect XAI to be a standard requirement. It will help drive innovation, transparency, and worthiness.
XAI is likely to lead to stricter regulations, as industries and governments demand greater accountability for AI-driven decisions.
Generative AI
The emergence of Generative AI has been the most exciting development in recent years. Generative AI enables machines to create new content, like text, images, and even code, based on patterns learned from existing data.
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Generative AI can be used to generate synthetic data, create new features, and automate data preparation. Generative AI can be used to engineer new features from existing data, potentially improving the performance of machine learning models.
Importance of Data Quality
As per the recent reports by Grand View Research, the global AI market is expected to grow from $136.6 billion in 2022 to $1597 billion by 2030. Since AI is in demand and people are using AI and ML models are critical to their performance, it is important to make sure that the data collected is consistent, complete, and accurate.
Ethical AI and ML
AI and ML models are becoming more prevalent and powerful, so it is important to consider the ethical implications as well. We need to understand that AI and ML models can produce biased results if they are only trained on data that is not representative of the population they are intended to serve.
AI and ML can be used to collect and analyze personal data, raising concerns about privacy and data protection. Sometimes, it can be difficult to understand how AI and ML models arrive at their decisions, which can make it challenging to assess their fairness and reliability.
Conclusion
With the blog, we got to know about the dynamic nature of data science and its potential to drive innovation for every sector. As AI and Machine Learning continue to evolve, accessibility and explainability will play a crucial role in shaping the future.
Be it a data scientist, business leader or enthusiast, staying up-to-date is a key to leverage the full potential of data science in the years ahead.
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