AUTOMATED MACHINE LEARNING MARKET SIZE AND SHARE ANALYSIS - GROWTH TRENDS AND FORECASTS (2024 - 2031)

Key Takeaways from Analyst

One of the key drivers for this growth is the increasing shortage of data science and machine learning experts. Automated machine learning tools allow organizations without dedicated data science teams to develop ML models. This expands the potential customer base for ML applications. Additionally, machine learning is becoming more central to business strategies across industries. Automation makes complex ML processes more accessible and speeds up experimentation and model development.

However, concerns around reliability and control over model building may restrain the early adoption of fully automated tools. Many companies want to balance automation with human oversight in ML processes. Additionally, integrating automated systems with existing enterprise IT infrastructures can be challenging.

North America currently dominates the automated ML space due to widespread early adoptions among technology companies. However, Asian markets, especially China, are expected to see faster growth. This is because of increasing government support for AI development and growing investments by technology giants based in the region. In addition, large volumes of data and low-cost trials provide Asian players advantages to develop automated solutions tailored for their markets.

Overall, while fully-automated tools may not see universal adoption, having different levels of automation for different ML tasks will unlock new use cases.