DevOps methodologies are increasingly generating vast amounts of data throughout the application lifecycle, necessitating robust monitoring and analysis to achieve full automation. The integration of machine learning (ML) into DevOps can reduce noise, enhance predictive capabilities, and improve operations, but its adoption is limited due to the complexity of ML and a skills gap among DevOps practitioners. The traditional threshold approach in monitoring results in high alert fatigue, whereas ML offers a more mathematical and proactive solution. However, the black-box nature of many ML tools and the lack of understanding of advanced mathematical concepts among DevOps engineers pose significant challenges. Organizational hurdles, such as assembling multidisciplinary teams and managing complex projects, further hinder ML's integration into DevOps. Despite these obstacles, the demand for ML in DevOps is expected to grow as frameworks become more accessible, more professionals are trained, and companies like Google and Facebook continue to develop user-friendly tools. As a result, enterprises are investing in training and recruiting to enhance their teams' ML expertise, recognizing the substantial benefits ML can bring to business processes.