## 5 Conclusions  
ML applications have sparked significant interest in O-RAN for their potential to revolutionize network automation. By exploiting ML to analyze vast amounts of network data in real-time, identifying performance issues and optimizing network parameters on the fly, ML is expected to facilitate predictive maintenance, intelligent resource allocation, and network optimization. Furthermore, predictive analytics can help anticipate and prevent network failures, reducing downtime and maintenance costs. Ultimately, ML holds the promise of making O-RAN networks more efficient, reliable, and responsive to the dynamic demands of modern communication environments. In this paper, focusing on network automation in O-RAN using ML techniques, we first presented the design principles and architecture of O-RAN, highlighted the openness and disaggregation of RAN components, and its capability to extend network operation with native ML support. The current research landscape of ML applications in O-RAN was then presented. Several key aspects of network management were surveyed, including session and user access management, radio resource management, network slicing, mobility and traffic management, energy efficiency, O-RAN component deployment and function splits, ML workflow management, and support for NTN and satellite networks. For instance, ML can play a pivotal role in automating fault detection and recovery processes by meticulously analyzing data traffic and identifying anomalies. This reduces the necessity for manual intervention, subsequently bolstering network resilience. ML can also prove invaluable in the realms of capacity planning and elevating customer experiences. By forecasting network traffic growth, aiding in optimizing capacity, and analyzing user behavior and feedback, ML can contribute to a more efficient and customer-centric network management approach. However, numerous challenges must be surmounted to unlock the full potential of ML in propelling O-RAN network forward. Upon identifying several pivotal research domains, these challenges manifest across diverse realms encompassing data collection and analysis, as well as the development, deployment, maintenance, and operation of ML models. A fundamental hurdle arises from the various types of data emanating from different vendor equipment, necessitating harmonization for effective data analysis. Utilization of synthetic data collectible from network simulators could be also considered when real-world empirical data are difficult to obtain. Once data acquisition hurdles are overcome, data-driven decision-making processes must be thoughtfully tailored for target optimizations, such as enhancing network capacity and mitigating interference. In complex scenarios (e.g., O-RAN-including UAVs), consideration of multiple objectives would be required simultaneously, such as optimizing the trajectory of UAVs and task offloading among heterogeneous O-RAN components, including UAVs. The multifaceted nature of O-RAN components provide various options for deploying ML models, prompting careful deliberation on compatible components and interface for ML model and data exchange. When multiple components are selected for ML model deployment, the level of coordination and cooperation between components should be also investigated. Furthermore, the dependable operation of multiple ML modules such as xApps, rApps, and dApps within O-RAN Ric, mandates unwavering attention to reliability. Equally critical is the dynamic and autonomous responsiveness of deployed ML models to ever-changingnetwork environments. Furthermore, we discussed the issue to find the balance between data accessibility for ML and maintaining robust data security. These challenges, when conquered, will usher in an era where ML empowers O-RAN network automation, leading to heightened efficiency, reduced operational costs, fortified security, and an enriched user experience.