As you work with Azure Machine Learning, it’s important to carefully plan and manage your costs to ensure your projects stay on budget. In this comprehensive guide, we’ll walk through several strategies and best practices to help you optimize your spending and get the most value from your Azure investment.

Estimate Costs Upfront

Before you start deploying resources, use the Azure Pricing Calculator to estimate the potential costs. This tool allows you to build out your Azure Machine Learning workspace and other supporting services, and it will provide an estimate of the monthly charges you can expect.

As you add resources to your workspace, be sure to return to the calculator and update the configuration to get the latest cost projections. This will help you plan your budget and avoid unexpected charges down the line.

Understand the Full Billing Model

When you create an Azure Machine Learning workspace, additional Azure services are automatically provisioned to support the core functionality. These include:

The costs for these supporting services will be included in your overall Azure bill, so it’s important to factor them in when estimating your total spend.

Additionally, there are some resources that may continue to accrue costs even after you’ve stopped actively using your Azure Machine Learning workspace, such as:

  • Virtual machines
  • Load balancers
  • Azure virtual networks
  • Bandwidth costs

Be sure to delete any unused resources to avoid unwanted charges. You can also use features like managed virtual networks and idle shutdown to help control costs.

Monitor Costs in the Azure Portal

Once you start using Azure Machine Learning, you can use the built-in cost analysis tools in the Azure portal to monitor your spending:

  1. Sign into the Azure portal and navigate to the Cost Analysis service.
  2. Select the appropriate scope, such as your subscription or resource group.
  3. In the cost analysis view, you can see your Azure Machine Learning costs broken down by various dimensions like service, location, and resource group.

This gives you visibility into where your money is being spent so you can identify areas to optimize. You can also set up budgets and alerts to proactively manage your costs and stay within your target spending.

Optimize Costs

There are several strategies you can use to reduce your Azure Machine Learning costs:

  • Configure autoscaling: Set up autoscaling on your training clusters and managed online endpoints to ensure you’re only using the compute resources you need.
  • Leverage low-priority VMs: Use low-priority virtual machines for your training workloads to get significant cost savings.
  • Schedule compute resources: Automatically start and stop your compute instances based on your usage patterns to avoid unnecessary charges.
  • Use Azure Reserved Instances: Purchase one-year or three-year reserved instances for your persistent compute needs to lock in discounted rates.
  • Train locally when possible: For small-scale experimentation and development, consider training your models locally to avoid cloud compute costs.
  • Parallelize training: Break down your training workloads into parallel tasks to improve efficiency and reduce overall compute time.
  • Set data retention policies: Implement policies to automatically delete old data and models you no longer need.
  • Deploy to the same region: Keeping your Azure resources in the same Azure region can help minimize network transfer charges.

For a more detailed look at cost optimization strategies, check out the Manage and optimize Azure Machine Learning costs guide.

Stay in Control of Your Spending

Effectively managing costs for Azure Machine Learning is an ongoing process, but the techniques covered in this article will help you stay in control of your spending and get the most value from your cloud investment. Remember to revisit your cost estimates and optimization strategies regularly as your usage and needs evolve over time.

Sources: