The usage of Azure credits can vary depending on the specific resources and services you use. Without more information about the activities you performed in the Azure Machine Learning Studio, it is difficult to determine the exact reason for the quick depletion of your credits.
However, it is possible that certain actions or services in Azure Machine Learning Studio, such as running experiments, training models, or using compute resources, can consume credits relatively quickly, especially if you are performing resource-intensive tasks or working with large datasets.
To ensure that you have enough credits to complete the challenge for free, it's important to monitor your credit usage regularly. You can use Azure Cost Management and Billing to track your usage and get insights into how your credits are being consumed. This will help you identify any potential areas where you can optimize your resource usage and manage your credits effectively.
Additionally, consider taking the following steps to optimize your credit usage:
Deallocate compute resources: When you're not actively using compute resources, make sure to deallocate or stop them to avoid unnecessary charges.
Use appropriate instance types: Choose the right instance type based on your workload requirements. Using lower-cost instances where applicable can help reduce credit consumption.
Delete unnecessary resources: Remove any unused or unnecessary resources, such as experiments, models, or data files, to free up credits.
Set spending limits: Set up spending caps or budgets within Azure Cost Management to receive notifications when you approach your credit limit.
By monitoring your usage, optimizing resource usage, and setting spending limits, you can maximize the usage of your credits and complete the challenge within the available credits.