Merge pull request MicrosoftDocs#93044 from changeworld/patch-70

Fix typo

Author: Jak-MS

articles/machine-learning/concept-open-source.md

@@ -97,6 +97,6 @@ Securing deployments is an important part of the deployment process. To [deploy
 
 Machine Learning Operations (MLOps), commonly thought of as DevOps for machine learning allows you to build more transparent, resilient, and reproducible machine learning workflows. See the [what is MLOps article](./concept-model-management-and-deployment.md) to learn more about MLOps. 
 
-Using DevOps practices like continuous integration (CI) and continuous deployment (CD), you can automate the end-to-end machine learning lifecycle and capture governance data around it. You can define your [machine learning CI/CD pipeline in GitHub actions](./how-to-github-actions-machine-learning.md) to run Azure Machine Learning training and deployment tasks. 
+Using DevOps practices like continuous integration (CI) and continuous deployment (CD), you can automate the end-to-end machine learning lifecycle and capture governance data around it. You can define your [machine learning CI/CD pipeline in GitHub Actions](./how-to-github-actions-machine-learning.md) to run Azure Machine Learning training and deployment tasks. 
 
 Capturing software dependencies, metrics, metadata, data and model versioning are an important part of the MLOps process in order to build transparent, reproducible, and auditable pipelines. For this task, you can [use MLFlow in Azure Machine Learning](how-to-use-mlflow.md) as well as when [training machine learning models in Azure Databricks](./how-to-use-mlflow-azure-databricks.md). You can also [deploy MLflow models as an Azure web service](how-to-deploy-mlflow-models.md).