Update Blog “end-to-end-easy-to-use-pipeline-for-training-a-model-on-medmnist-v2-using-hpe-machine-learning-development-environment-flask”

Author: alvx23

content/blog/end-to-end-easy-to-use-pipeline-for-training-a-model-on-medmnist-v2-using-hpe-machine-learning-development-environment-flask.md

@@ -45,7 +45,7 @@ In this blog post, you'll get to see firsthand how HPE Machine Learning Developm
 
 If you are interested in more details about how this example was developed, take a look at the "Practice" section. For a full, in-depth, model porting guide, check out this [model porting guide.](https://docs.determined.ai/latest/tutorials/pytorch-porting-tutorial.html) The code for this example and the instructions used to run it can be found in the [repository](https://github.com/ighodgao/determined_medmnist_e2e).
 
-| *Feature*                | *Without HPE Machine Learning Development Environment*                                                                                                | *With HPE Machine Learning Development Environment*                                                                                                         |
+| **Feature**              | **Without HPE Machine Learning Development Environment**                                                                                              | **With HPE Machine Learning Development Environment**                                                                                                       |
 | ------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | Distributed Training     | Configure using open-source tools of your choice (e.g. Ray, Horovod)                                                                                  | Fault tolerant distributed training automatically enabled                                                                                                   |
 | Experiment Visualization | Write custom code or configure using open-source tools of your choice, (e.g. Weights & Biases, Tensorboard)                                           | Training metrics (model accuracy, model loss) available natively in WebUI, including Tensorboard extension                                                  |