The global MLOps market is projected to reach $39 billion by 2034, with financial services at the forefront of this expansion, although they face significant compliance challenges. Machine learning model failures in this sector can result in severe economic penalties and reputational damage, necessitating robust compliance strategies. Key strategies for addressing these challenges include establishing strong model governance, implementing policy-driven CI/CD pipelines, monitoring model performance, enforcing data lineage, automating compliance tests, and securing ML pipelines. These strategies ensure transparency, fairness, and rigorous compliance with evolving regulatory frameworks, thereby transforming audit preparation from a reactive burden into a proactive, competitive advantage. Tools like Galileo's Agent Observability Platform are highlighted as essential for achieving comprehensive governance and compliance in regulated industries, offering features such as real-time architecture monitoring and comprehensive audit trails.