What is Kanban for ML?
Kanban for ML visualizes machine learning workflow stages from data exploration through model deployment using a Kanban board, enabling teams to manage work-in-progress limits, identify bottlenecks in the ML pipeline, and optimize flow of experiments, models, and features through development to production.
This glossary term is currently being developed. Detailed content covering implementation approaches, best practices, common challenges, and business applications will be added soon. For immediate assistance with AI project management, please contact Pertama Partners for advisory services.
Kanban for ML provides transparency into AI project progress that traditional status reports consistently fail to convey, reducing stakeholder surprise by 60%. The visual workflow management catches pipeline bottlenecks before they cascade into missed deadlines, typically saving 2-3 weeks per quarterly release cycle. This lightweight process framework also helps mid-market companies manage AI work without the overhead of dedicated project management tooling.
- Define workflow stages specific to ML: data prep, feature engineering, model training, evaluation, deployment
- Set WIP limits that account for long-running experiments and model training cycles
- Track cycle time from experiment design through production deployment
- Visualize blocked experiments, data quality issues, and deployment bottlenecks
- Include stages for model monitoring, retraining, and performance optimization
- Optimize flow by addressing bottlenecks in data pipelines, compute resources, or approval gates
- Add explicit WIP limits for experiment columns to prevent data scientists from running too many parallel trials, which fragments attention and delays conclusive results.
- Create dedicated swim lanes for data preparation, model training, and deployment stages since bottlenecks appear at different pipeline stages depending on project maturity.
- Visualize blocked items with root cause labels to surface recurring infrastructure dependencies that slow ML delivery across multiple concurrent projects.
- Add explicit WIP limits for experiment columns to prevent data scientists from running too many parallel trials, which fragments attention and delays conclusive results.
- Create dedicated swim lanes for data preparation, model training, and deployment stages since bottlenecks appear at different pipeline stages depending on project maturity.
- Visualize blocked items with root cause labels to surface recurring infrastructure dependencies that slow ML delivery across multiple concurrent projects.
Common Questions
How does this apply to AI projects specifically?
AI projects have unique characteristics including data dependencies, model uncertainty, and iterative development cycles that require adapted project management approaches.
What are common challenges with this in AI projects?
Common challenges include managing stakeholder expectations around AI capabilities, balancing exploration with delivery timelines, and maintaining project momentum through experimentation phases.
More Questions
Various tools and frameworks can support this practice. Consult with project management experts to select approaches suited to your organization's AI maturity and project complexity.
References
- NIST Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology (NIST) (2023). View source
- Stanford HAI AI Index Report 2025. Stanford Institute for Human-Centered AI (2025). View source
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