Secure multi-party computation (SMPC) is a cryptographic technique that enables multiple parties to work together on data without sharing their private information with each other. This paper investigates how two open-source frameworks, SecretFlow and FATE, implement SMPC and other privacy-preserving techniques and evaluates their usability and scalability in applied settings. The goal is to better understand the trade-offs these frameworks offer for researchers and developers seeking to build privacy-preserving machine learning pipelines. Using a small-scale experimental setup, both frameworks were deployed in controlled environments and evaluated based on setup complexity, documentation quality, modularity, architecture, and the secure computation methods used. The results show that SecretFlow offers greater SMPC flexibility, modularity, and developer usability, making it well-suited for research and rapid prototyping. FATE, while more complex to integrate, provides comprehensive workflow coordination and is better suited for production environments. Experiments were conducted on a single physical node with a small dataset, enabling reproducibility but limiting generalizability to large-scale, real-world deployments. This work provides practical insights into the usability and architectural design of the frameworks, along with a protocol-level comparison to guide developers in selecting tools for privacy-sensitive machine learning.