Navigating the MLOps Journey: Key Considerations for Successful Implementations - Akanksha Malik

MLOps requires extensive planning and documentation to ensure sustainable machine learning implementations that don’t end up as abandoned POCs

Team structure and clear roles/responsibilities between data engineers, ML engineers and data scientists are crucial for successful MLOps

Environment management (dev/test/prod) with proper access controls and resource governance helps prevent costly mistakes and security issues

Data governance, sovereignty and regional considerations need to be evaluated early, especially for global deployments

Model monitoring, versioning and rollback capabilities are essential for maintaining trust and reliability

Resource estimation and cost management are key - ML infrastructure costs can quickly spiral without proper controls

Regular model retraining schedules should align with business needs rather than arbitrary schedules

Documentation of decisions, configurations and processes helps with knowledge retention as team members change

Automation through pipelines reduces manual work but requires upfront investment in frameworks and testing

The “best” MLOps solution depends heavily on team capabilities, business context and existing infrastructure rather than following trendy tools