How to manage the costs of your data science environment and models in Azure?

Tools to manage data costs 

So how do we control and manage the costs in Azure? Luckily, Microsoft has developed some tools to guide you. We will discuss Azure Pricing Calculator, Azure Advisor, and Cost Management in the Azure portal.

Azure Pricing Calculator 

First and foremost, before deploying any services to your Azure environment, it’s advised to use the Microsoft Azure Pricing Calculator. This is the perfect tool to plan incurred costs. In this case, we focus on the expenses relating to your data science environment and models. Hence, if you click on the Azure Pricing Calculator link, you will see your cost estimation for deploying Azure Machine Learning (AML) as your data science environment. Other services you need can be added to the pricing calculator to estimate the overall costs you can expect.

Azure Advisor 

Azure Advisor is a tool and service focused on all costs incurred by your Azure usage. It focuses on optimizing, reducing costs, and identifying over or underutilized resources you've deployed. This also counts for your data science environment on Azure. For example, it can help you identify the underutilization of computing in virtual machines. It can also give you recommendations for your retention policy of storage of log data. In the end, there are many options to ensure you incur fewer costs and optimize your environment.

Cost Management

Cost Management + Billing in Azure gives you tools to play around with. So you’ll understand your costs and be able to analyze and report on them. This is all to reduce your Azure spend and get the most out of your Azure investments.

To analyze your costs, you can create a cost analysis. You’ll get an in-depth analysis of your costs allowing you to dissect every penny spent on your resource properties. Azure also has a Cost Details API, which allows you to pull any raw data out of your Azure environment. 

A logical consequence, once you’ve analyzed the costs and patterns of your spending, is to induce budgets. Budgets can be related to actual expenses and come with the ability to set thresholds and create alerts. It is possible that you can configure a trigger based on a certain threshold of your budget, which then shuts down a service or moves you to a different pricing tier.

Now that we've gone through some tools, we want to help you ensure you have your costs under control, so no more unexpected bills are coming your way!

Identify expected cost sources with Azure Machine Learning 

So, how to manage the costs in your actual environment? Let's consider an often-used Data science tool on Azure: Azure Machine Learning. When deploying Azure Machine Learning, you will find that many factors influence your costs as it’s a pay-as-you-go structure. Examples are the compute instance used, processing time, the region, and the number of deployed instances.

In addition, you will find that AML by itself does not create a full data science solution. You will have to determine what resources you already have and need. Then you will have to identify the costs of these resources. In Azure, you might also need other resources, such as data storage or third-party services. All these tools are, of course, not free. Hence, to better understand the estimation of the total costs of the environment you should review what you have in-house, and what you need. It can also be helpful to check the cost-related documentation of the services using the tools mentioned in the previous topic.

How Intercept tackles the move to Azure   

Have we yet convinced you of the importance of cost management of your data science solutions on Azure? We certainly hope so! Still trying to figure out where to start? At Intercept, we can’t wait to help you. Together with our Data Scientist, we can guide you through your requirements, helping you through every step of your data journey. Take the first steps with our second opinion.