Develop code by using a compute instance

To develop code by using a compute instance for the exam “Designing and Implementing a Data Science Solution on Azure,” you will need to have a good understanding of Azure Machine Learning and computing resources. In this article, we will explore how to leverage a compute instance to develop and run code for data science solutions on Azure.

Creating a Compute Instance

A compute instance in Azure Machine Learning is a fully managed cloud-based workstation that provides a development environment for data scientists. It comes pre-configured with various data science tools and libraries, making it an ideal choice for coding, experimenting, and collaboration.

To get started, let’s create a compute instance in Azure Machine Learning Studio. Follow these steps:

1. Sign in to the Azure portal at http://portal.azure.com.
2. Open Azure Machine Learning Studio.
3. Navigate to the Compute tab.
4. Click on Compute Instances.
5. Click on “+ New” to create a new compute instance.
6. Provide a unique name for the compute instance and select the desired region.
7. Choose a virtual machine size based on your requirements. You can start with a lower-end instance and scale up as needed.
8. Set the maximum number of compute instance replicas that can be created. This helps manage costs and ensures availability.
9. Enable SSH access if required for remote connectivity.
10. Click “Create” to provision the compute instance.

Once the compute instance is provisioned, you can access it by clicking on its name under Compute Instances. This will open the JupyterLab interface, which provides a powerful environment for coding and experimentation.

Developing and Running Code

Now let’s explore how to develop and run code using the compute instance. In the JupyterLab interface, you can create new Jupyter notebooks or Python scripts. To create a new notebook, follow these steps:

1. Click on “File” in the JupyterLab navigation menu.
2. Choose “New” and select “Notebook” or “Python 3” under Notebook.
3. This will open a new notebook where you can write and execute code cells.

To run code using the compute instance, you can simply write code in a notebook cell and execute it by clicking on the “Run” button in the notebook toolbar. You can also use keyboard shortcuts to execute code cells (e.g., Shift+Enter).

Here’s an example of running a Python code cell that imports the pandas library and reads a CSV file:

import pandas as pd

# Display the first 5 rows
data.head()

In addition to executing code cells, you can also install additional Python packages, manage dependencies, and work with code files using the compute instance. For example, to install a package, you can run a code cell with the following command:

!pip install package_name

Furthermore, you can upload and download files to and from the compute instance using the JupyterLab interface. This allows you to access and process data files within your code.

Conclusion

A compute instance in Azure Machine Learning provides an excellent environment for developing and running code for data science solutions on Azure. You can leverage its capabilities to write code, experiment with different algorithms, and collaborate with team members. By following the steps outlined in this article, you can start utilizing a compute instance to prepare for the exam “Designing and Implementing a Data Science Solution on Azure.”