Integrating Scrum and MLOps: Adapting Agile Methodologies to the Machine Learning Lifecycle - Michał Opalski/ai-agile.org
Abstract
The growing adoption of machine learning (ML) systems in production environments has exposed significant limitations of traditional Agile methodologies, particularly Scrum, when applied to data-driven and non-deterministic development processes. While MLOps has emerged as a set of engineering practices addressing deployment, monitoring, and reproducibility of ML models, it largely lacks an explicit integration with project management and Agile governance frameworks. This paper addresses this gap by analyzing the fundamental misalignments between Scrum and the machine learning lifecycle and proposing an integrated Scrum–MLOps framework. The proposed model reinterprets Scrum artifacts, roles, and metrics to accommodate experimental workflows, data-centric development, and continuous model evolution. We introduce novel sprint-level metrics tailored for ML projects and discuss empirical validation strategies through case studies. The results suggest that aligning Scrum with MLOps principles enables more predictable delivery, improved transparency, and faster time-to-value in production-grade AI systems.
Keywords: Agile, Scrum, MLOps, Machine Learning Engineering, AI Project Management
1. Introduction
Machine learning has transitioned from a research-oriented discipline to a core component of modern software systems. Organizations increasingly rely on ML models to support decision-making, automation, and personalization across domains such as finance, healthcare, manufacturing, and e-commerce. However, the development of machine learning systems differs fundamentally from traditional software engineering. ML systems are probabilistic, data-dependent, and continuously evolving, which introduces high levels of uncertainty throughout the development lifecycle.
Agile methodologies—particularly Scrum—have become the dominant paradigm for managing software development projects due to their emphasis on iterative delivery, customer feedback, and adaptability to change. Despite their success in deterministic software environments, these methodologies often struggle when applied to machine learning projects. Teams frequently encounter challenges related to sprint predictability, definition of done, estimation, and value measurement.
In parallel, MLOps has emerged as a discipline focused on operationalizing machine learning models through automation, reproducibility, continuous integration, deployment, and monitoring. While MLOps addresses critical technical challenges, it does not inherently solve organizational and process-related issues associated with managing ML projects.
This paper argues that neither Scrum nor MLOps alone is sufficient to manage the full complexity of machine learning systems. We propose an integrated Scrum–MLOps framework that aligns Agile principles with the realities of ML engineering. The paper contributes a conceptual model, adapted Scrum artifacts, and ML-specific sprint metrics, providing both theoretical and practical value.
2. Background and Related Work
2.1 Scrum and Agile Software Development
Scrum is an empirical process framework based on transparency, inspection, and adaptation. It structures work into time-boxed iterations called sprints, each producing a potentially shippable increment of product functionality. Scrum assumes that requirements can be incrementally refined and that the development process is sufficiently predictable to allow estimation and planning.
While Scrum has been successfully applied to a wide range of software projects, prior research highlights its limitations in exploratory and research-intensive contexts. Machine learning development, which involves hypothesis testing, experimentation, and iterative learning, often violates the assumptions underlying Scrum’s predictability and incremental value delivery.
2.2 Machine Learning Lifecycle
The machine learning lifecycle typically includes data collection, data preprocessing, feature engineering, model training, evaluation, deployment, and monitoring. Unlike traditional software, the behavior of ML systems is learned from data rather than explicitly programmed. Model performance depends heavily on data quality, distribution, and temporal stability.
Frameworks such as CRISP-DM and KDD provide high-level guidance but do not fully address modern production requirements such as continuous retraining, data drift detection, and model governance.
2.3 MLOps
MLOps extends DevOps principles to machine learning systems by emphasizing automation, reproducibility, and lifecycle management. Core components of MLOps include data versioning, experiment tracking, continuous training (CT), model registries, and production monitoring.
Despite its technical maturity, MLOps literature often treats project management as an external concern, assuming that organizational processes are already in place. This separation leads to friction between engineering workflows and Agile governance structures.
2.4 Existing Attempts at Agile–ML Integration
Prior approaches such as Agile Data Science and Data-Driven Scrum propose adaptations to Agile practices for data-intensive projects. However, these approaches often lack formalization, empirical validation, or integration with modern MLOps tooling.
3. Problem Analysis: Scrum–ML Misalignment
Applying Scrum directly to ML projects exposes several structural conflicts:
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Sprint Goals: In ML, achieving a specific performance improvement within a sprint cannot be guaranteed.
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Definition of Done: A model may meet technical criteria but fail to generalize in production.
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Estimation: Experimental tasks have inherently uncertain outcomes and durations.
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Increment Value: Knowledge gained from failed experiments may not translate into immediately deployable features.
These issues often result in artificial compliance with Scrum rituals, reduced transparency, and erosion of stakeholder trust.
4. Research Objectives and Methodology
The objectives of this study are:
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To analyze the incompatibilities between Scrum and ML workflows.
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To propose an integrated Scrum–MLOps framework.
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To redefine Scrum artifacts and metrics for ML projects.
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To outline empirical validation strategies.
The research follows a design science approach, combining conceptual modeling with empirical grounding through proposed case study methodologies.
5. The Scrum–MLOps Integrated Framework
5.1 Iteration Structure
The framework introduces three complementary sprint types:
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Data Sprints: Focused on data acquisition, cleaning, labeling, and feature engineering.
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Model Sprints: Centered on experimentation, training, hyperparameter tuning, and validation.
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Deployment Sprints: Dedicated to integration, deployment, monitoring, and retraining pipelines.
These sprints may occur sequentially or in parallel, depending on project maturity.
5.2 Adaptation of Scrum Artifacts
5.3 Integration with MLOps Pipelines
Each sprint is supported by automated MLOps pipelines ensuring:
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Experiment reproducibility
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Continuous integration and testing
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Continuous training and deployment
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Monitoring and feedback loops
Automation becomes a prerequisite for empirical Scrum inspection.
6. ML-Specific Sprint Metrics
Traditional velocity metrics are insufficient for ML projects. We propose the following ML Sprint Metrics:
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Data quality improvement delta
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Model stability and variance
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Sensitivity to data drift
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Time-to-retrain
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Proxy business impact metrics
These metrics prioritize learning efficiency and system robustness over short-term output.
7. Validation and Case Study Design
Empirical validation may involve:
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Comparative case studies of teams using traditional Scrum versus Scrum–MLOps
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Longitudinal analysis of sprint metrics
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Qualitative interviews with data scientists and ML engineers
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Observation of sprint reviews and retrospectives
8. Discussion
The proposed framework improves transparency and alignment between technical and organizational dimensions of ML development. However, it requires higher team maturity, strong automation capabilities, and organizational support. There is also a risk of superficial adoption without genuine process change.
9. Conclusions and Future Work
This paper demonstrates that effective management of machine learning projects requires a fundamental rethinking of Agile methodologies. By integrating Scrum with MLOps, organizations can better manage uncertainty, accelerate learning, and deliver sustainable AI systems.
Future research should focus on large-scale empirical validation, tooling support, and integration with scaled Agile frameworks.
