diff --git a/contents/ops/ops.qmd b/contents/ops/ops.qmd
index 5b39c06b..75eec99d 100644
--- a/contents/ops/ops.qmd
+++ b/contents/ops/ops.qmd
@@ -149,9 +149,9 @@ Continuous integration and continuous delivery (CI/CD) pipelines actively automa
 
 CI/CD pipelines orchestrate key steps, including checking out new code changes, transforming data, training and registering new models, validation testing, containerization, deploying to environments like staging clusters, and promoting to production. Teams leverage popular CI/CD solutions like [Jenkins](https://www.jenkins.io/), [CircleCI](https://circleci.com/) and [GitHub Actions](https://github.com/features/actions) to execute these MLOps pipelines, while [Prefect](https://www.prefect.io/), [Metaflow](https://metaflow.org/) and [Kubeflow](https://www.kubeflow.org/) offer ML-focused options.
 
-@fig-ci-cd illustrates a CI/CD pipeline specifically tailored for MLOps. The process starts with a dataset and feature repository (on the left), which feeds into a dataset ingestion stage. Post-ingestion, the data undergoes validation to ensure its quality before being transformed for training. Parallel to this, a retraining trigger can initiate the pipeline based on specified criteria. The data then passes through a model training/tuning phase within a data processing engine, followed by model evaluation and validation. Once validated, the model is registered and stored in a machine learning metadata and artifact repository. The final stage involves deploying the trained model back into the dataset and feature repository, thereby creating a cyclical process for continuous improvement and deployment of machine learning models.
+@fig-ops-cicd illustrates a CI/CD pipeline specifically tailored for MLOps. The process starts with a dataset and feature repository (on the left), which feeds into a dataset ingestion stage. Post-ingestion, the data undergoes validation to ensure its quality before being transformed for training. Parallel to this, a retraining trigger can initiate the pipeline based on specified criteria. The data then passes through a model training/tuning phase within a data processing engine, followed by model evaluation and validation. Once validated, the model is registered and stored in a machine learning metadata and artifact repository. The final stage involves deploying the trained model back into the dataset and feature repository, thereby creating a cyclical process for continuous improvement and deployment of machine learning models.
 
-![MLOps CI/CD diagram. Source: HarvardX.](images/png/cicd_pipelines.png){#fig-ci-cd}
+![MLOps CI/CD diagram. Source: HarvardX.](images/png/cicd_pipelines.png){#fig-ops-cicd}
 
 For example, when a data scientist checks improvements to an image classification model into a [GitHub](https://github.com/) repository, this actively triggers a Jenkins CI/CD pipeline. The pipeline reruns data transformations and model training on the latest data, tracking experiments with [MLflow](https://mlflow.org/). After automated validation testing, teams deploy the model container to a [Kubernetes](https://kubernetes.io/) staging cluster for further QA. Once approved, Jenkins facilitates a phased rollout of the model to production with [canary deployments](https://kubernetes.io/docs/concepts/cluster-administration/manage-deployment/#canary-deployments) to catch any issues. If anomalies are detected, the pipeline enables teams to roll back to the previous model version gracefully.