The steps to create a machine learning pipeline in Azure Machine Learning Workspace:

1. Create an Azure Machine Learning Workspace:

○ If you don't have one already, create an Azure Machine Learning workspace. This serves as the central hub for managing your machine learning resources.

2. Set Up Datastores:

○ Datastores allow you to access data needed in your pipeline. By default, each workspace has a default datastore connected to Azure Blob storage. You can register additional datastores if necessary [4].

3. Define Your Pipeline Steps:

○ Break down your ML task into manageable components (steps). Common steps include data preparation, model training, and evaluation.

○ Use the Azure Machine Learning SDK to create these steps. You can define them as PythonScriptStep or other relevant step types.

4. Configure Compute Targets:

○ Set up the compute targets where your pipeline steps will run. Options include Azure Machine Learning Compute, Azure Databricks, or other compute resources.

5. Orchestrate the Pipeline:

○ Use the Azure Machine Learning pipeline service to automatically manage dependencies between steps.

○ Specify the order in which steps should execute and how they interact.

6. Publish the Pipeline:

○ Once your pipeline is ready, publish it. This makes it accessible for later use or sharing with others.

7. Monitor and Track Performance:

○ Monitor your pipeline's performance in real-world scenarios.

○ Detect data drift and adjust your pipeline as needed.

This workspace provides an environment to create and manage the end-to-end life cycle of Machine Learning models. Unlike general purpose software, Azure machine learning has significantly different requirements such as the use of a wide variety of technologies, libraries and frameworks, separation of training and testing phases before deploying and use of a model and iterations for model tuning independent of the model creation and training etc.  Azure Machine Learning’s compatibility with open-source frameworks and platforms like PyTorch and TensorFlow makes it an effective all-in-one platform for integrating and handling data and models which tremendously relieves the onus on the business to develop new capabilities. Azure Machine Learning is designed for all skill levels, with advanced MLOps features and simple no-code model creation and deployment.