Update concept-distributed-training.md

Author: ssalgadodev

articles/machine-learning/concept-distributed-training.md

@@ -21,7 +21,7 @@ In distributed training, the workload to train a model is split up and shared am
 
 ## Deep learning and distributed training
 
-There are two main types of distributed training: [data parallelism](#data-parallelism) and [model parallelism](#model-parallelism). For distributed training on deep learning models, the [Azure Machine Learning SDK in Python](/python/api/overview/azure/ml/intro) supports integrations with PyTorch and TensorFlow. Both are popular frameworks that employ data parallelism for distributed training, and can use [Horovod](https://horovod.readthedocs.io/en/latest/summary_include.html) to optimize compute speeds.
+There are two main types of distributed training: [data parallelism](#data-parallelism) and [model parallelism](#model-parallelism). For distributed training on deep learning models, the [Azure Machine Learning SDK in Python](https://github.com/Azure/azure-sdk-for-python/blob/main/README.md) supports integrations with PyTorch and TensorFlow. Both are popular frameworks that employ data parallelism for distributed training, and can use [Horovod](https://horovod.readthedocs.io/en/latest/summary_include.html) to optimize compute speeds.
 
 * [Distributed training with PyTorch](how-to-train-distributed-gpu.md#pytorch)