- AI and ML algorithms heavily rely on vast amounts of high-quality data for training and validation. Acquiring, cleaning, and annotating large datasets can be time-consuming and resource-intensive.
- Many AI and ML models, such as deep neural networks, are often considered black boxes because they lack transparency in their decision-making process.
- AI and ML applications become more complex and data-intensive, scalability and efficiency become critical.
- AI systems should be capable of learning from new data and adapting to changing environments. Building algorithms that can continually learn, improve, and update themselves without human intervention is an ongoing challenge.
