Fix image syntax and add lightbox

Author: lgayhardt

articles/machine-learning/concept-differential-privacy.md

@@ -23,8 +23,7 @@ As the amount of data that an organization collects and uses for analyses increa
 
 Differential privacy is a set of systems and practices that help keep the data of individuals safe and private. In machine learning solutions, differential privacy may be required for regulatory compliance.
 
-> [!div class="mx-imgBorder"]
-> ![Differential privacy machine learning process](./media/concept-differential-privacy/differential-privacy-machine-learning.jpg)
+:::image type="content" source="./media/concept-differential-privacy/differential-privacy-machine-learning.jpg" alt-text="Differential privacy machine learning process." lightbox ="./media/concept-differential-privacy/differential-privacy-machine-learning.jpg":::
 
 In traditional scenarios, raw data is stored in files and databases. When users analyze data, they typically use the raw data. This is a concern because it might infringe on an individual's privacy. Differential privacy tries to deal with this problem by adding "noise" or randomness to the data so that users can't identify any individual data points. At the least, such a system provides plausible deniability. Therefore, the privacy of individuals is preserved with limited impact on the accuracy of the data.
 
@@ -80,4 +79,4 @@ Learn more about differential privacy in machine learning:
 
  - [How to build a differentially private system](how-to-differential-privacy.md) in Azure Machine Learning.
 
- - To learn more about the components of SmartNoise, check out the GitHub repositories for [SmartNoise Core](https://github.com/opendifferentialprivacy/smartnoise-core), [SmartNoise SDK](https://github.com/opendifferentialprivacy/smartnoise-sdk), and [SmartNoise samples](https://github.com/opendifferentialprivacy/smartnoise-samples).
+ - To learn more about the components of SmartNoise, check out the GitHub repositories for [SmartNoise Core](https://github.com/opendifferentialprivacy/smartnoise-core), [SmartNoise SDK](https://github.com/opendifferentialprivacy/smartnoise-sdk), and [SmartNoise samples](https://github.com/opendifferentialprivacy/smartnoise-samples).