As machine learning moves from experimental notebooks to production systems driving business-critical decisions, the operational maturity of ML infrastructure becomes a key factor in technology valuations. MLOps, the practice of operationalizing machine learning, encompasses model development, training, deployment, monitoring, and governance. During technical due diligence, assessing MLOps maturity reveals whether a company's AI capabilities are sustainable and scalable or fragile and dependent on individual contributors.
Model Development and Experimentation
Evaluate the tools and processes used for model development and experimentation. Determine whether the team uses experiment tracking platforms such as MLflow, Weights and Biases, or Neptune to log hyperparameters, metrics, and artifacts. Teams that lack systematic experiment tracking often cannot reproduce their results or explain why specific model architectures were chosen.
Assess the feature engineering pipeline, including how features are computed, stored, and served. Determine whether a feature store is in use and whether features are shared across models and teams. Duplicated feature engineering logic across different models creates inconsistency and maintenance burden that compounds over time.
Data versioning and lineage tracking are essential for reproducibility. Evaluate whether training datasets are versioned, whether data lineage is documented, and whether the team can reconstruct the exact data used to train any deployed model. Without data versioning, model debugging and regulatory compliance become extremely difficult.
Training and Deployment Pipelines
Assess the automation level of model training and deployment pipelines. At the lowest maturity level, models are trained manually in notebooks and deployed through ad-hoc processes. At higher maturity levels, training pipelines are automated, triggered by data changes or schedules, and deployments are managed through CI/CD pipelines with proper testing and validation gates.
Evaluate the model serving infrastructure, including whether models are served through REST APIs, batch inference jobs, or embedded in applications. Assess the latency, throughput, and availability characteristics of the serving infrastructure. Determine whether A/B testing and canary deployment mechanisms are in place for safely rolling out new model versions.
Model Monitoring and Drift Detection
Production model monitoring is one of the most important indicators of MLOps maturity. Evaluate whether the company monitors model performance metrics, data drift, concept drift, and prediction distribution changes in production. Models that are deployed without ongoing monitoring will silently degrade over time, potentially making increasingly poor decisions without anyone noticing.
Assess the alerting and response procedures for model performance degradation. When drift is detected or model accuracy drops below acceptable thresholds, what happens? Is there an automated retraining pipeline, or does the team rely on manual intervention? The time between detecting model degradation and deploying a corrected model is a critical metric for assessing operational maturity.
Evaluate how model predictions are logged and how ground truth labels are collected for ongoing model evaluation. Without a feedback loop that compares predictions to actual outcomes, there is no way to accurately measure model performance in production or identify systematic biases.
AI Governance and Risk Management
AI governance encompasses model documentation, fairness and bias assessment, explainability, and regulatory compliance. Evaluate whether model cards or similar documentation are maintained for each deployed model, describing its intended use, limitations, training data characteristics, and performance metrics across different populations.
Assess the company's approach to fairness and bias testing. Determine whether models are evaluated for disparate impact across protected categories and whether bias mitigation strategies are implemented. As AI regulation increases globally, companies without robust fairness assessment practices face growing regulatory and reputational risk that can materially impact deal value.