Unresolved Challenges in MLOps

This covers the major technical challenges that remain unresolved in the MLOps (Machine Learning Operations) ecosystem.

The main points of discussion are as follows:

• Model reliability and reproducibility: Consistency issues arising in the pipeline from model training to deployment
• Continuous monitoring and evaluation: The limitations of real-time evaluation systems in responding to changing data distributions (Data Drift)
• Scalability: Infrastructure requirements for efficiently managing large-scale models and complex workflows
• Collaboration and workflow integration: Seamless model lifecycle management between data scientists and engineers

This paper presents key research directions that must be addressed to advance the maturity of MLOps.