Final touches, added more details and links to how pipelines work
"\n",
"The notebooks acts as a template to,\n",
"1. Process a massive dataset in parallel by dividing the dataset into chunks using [DASK](https://dask.org/) .\n",
- "2. Perform distributed training on AzureML compute on these processed chunks."
+ "2. Perform distributed training on AzureML compute on these processed chunks.\n",
+ "\n",
+ "We create an [AzureML Pipeline](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines) for the two steps mentioned above. With this pipeline, the notebook can be scheduled regularly to fine tune BERT with new data and get a model which can be further deployed on [Azure Container Instance](https://docs.microsoft.com/en-us/azure/container-service/).\n",
+ "\n",
+ "AzureML Pipeline define reusable machine learning workflows that can be used as a template for your machine learning scenarios. Pipelines allow you to optimize your workflow and spend time on machine learning rather than infrastructure. If you are new to the concept of pipelines, [this would be a good place to get started](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines)."
]
},
{
