Developing an AIOps platform comes with several challenges. One of the biggest hurdles is data integration. AIOps platforms need to ingest vast amounts of structured and unstructured data from various sources such as logs, metrics, and events. Data normalization is crucial to ensure consistency and quality. Additionally, deploying machine learning models that can scale and accurately predict anomalies or potential failures is challenging. Continuous training of models to adapt to changing environments is another significant challenge. Furthermore, providing real-time analytics and actionable insights while minimizing false positives can be difficult. Security and compliance issues related to data privacy also need to be addressed during development.