Introduction:
In today’s digital age, machine learning has become an essential part of various industries. It has paved the way for innovative technological advancements that aim to simplify our daily routines. As a budding professional in the field of AI, it’s essential to acquire a certification that provides the foundational knowledge necessary to take on advanced challenges.
One such certification is the AI-900 certification, which provides an overview of Azure Cognitive Services, Bot Services, and other artificial intelligence services available on the Azure platform. However, to create a machine learning workspace to prepare for this certification, you need to follow a set of simple steps that we will discuss in this article.
Step 1: Create Azure account
The first step towards creating a machine learning workspace is to create an account on the Azure Portal. Azure is a cloud computing platform that enables us to build, test, deploy, and manage services and applications using Microsoft-managed data centers. Azure provides a free trial account that you can use to create your workspace.
Step 2: Access Azure Machine Learning Studio
Once you have created an account, the next step is to access Azure Machine Learning Studio. It is a cloud-based service that provides a drag and drops interface for creating machine learning models. You can use it to design your workspace, import data, and train machine learning models.
Step 3: Create a workspace
To create a workspace, click on ‘Create a resource’ in the Azure portal and search for ‘Azure Machine Learning.’ Select the option and provide the necessary details. You can choose from the available pricing tiers, depending upon your needs and get your workspace ready for use. You will also need to create a storage account for your workspace, which will store the data used for training and testing machine learning models.
Step 4: Import Data
Once you have created a workspace, the next step is to import data into it. You can use various data sources, including Azure Blob Storage, Azure SQL Database, and CSV files, to import data. After importing, you can preview the data, clean, and preprocess it, before training your machine learning model.
Step 5: Train and test your machine learning model
After importing data, you can now start training and testing your machine learning model. Azure Machine Learning Studio provides a drag and drops interface for designing your model, and you can also use Python scripts for advanced customizations. Once you have trained your model, you can test it using test data and evaluate its performance metrics.
Conclusion:
Creating a machine learning workspace is the first step towards obtaining the AI-900 certification. The above steps provide a guide for creating your workspace using Azure Machine Learning Studio. With Azure’s free trial account, creating a workspace has become more accessible, and building knowledge in machine learning has never been easier. By following these simple steps, you can create your workspace today and start building your machine learning expertise.
