Announcing our improved experiment tracking tools in Azure Machine Learning
Introduction
Experiment tracking is an essential part of the process of designing and building models in Azure Machine Learning. It is important for ensuring that experiments are properly tracked and monitored, and for providing a way to store and compare results over time. To make this process easier, Microsoft has announced the release of two new features for Azure Machine Learning: Experiment Tracking and Run History. This post will provide an overview of these new features, and explain how they can help streamline the machine learning process.
What is Experiment Tracking?
Experiment tracking is the process of recording the details of each experiment run in Azure Machine Learning. This includes the model type, hyperparameters, metrics, and other data related to the experiment. This data can then be used to compare results across different experiments, and to identify areas where improvements can be made. The Experiment Tracking feature allows users to easily track and compare experiments, without having to manually log the data.
What is Run History?
Run History is a feature that allows users to view a timeline of all the runs of a particular experiment. This includes dates, times, and details of each run, as well as any metrics that have been tracked. This feature makes it easier to identify trends and patterns, and to compare results over time.
Benefits of using Experiment Tracking and Run History
Using the Experiment Tracking and Run History features in Azure Machine Learning can help streamline the process of designing and building models. The ability to easily track and compare experiments can help to identify areas for improvement, and save time and effort in the process. Additionally, the Run History feature can help to identify trends and patterns in data, allowing users to quickly identify areas for further exploration.
How to get started with Experiment Tracking and Run History
Using Experiment Tracking and Run History in Azure Machine Learning is easy. All that is needed is an Azure Machine Learning workspace with an active subscription. Once the workspace is set up, users can begin to track their experiments and view their Run History.
Examples of Experiment Tracking and Run History in Action
To demonstrate the power of Experiment Tracking and Run History, let’s take a look at a real-world example. Suppose we are building a model for a customer segmentation task. We can use Experiment Tracking and Run History to track the results of our experiments and compare them over time. By doing this, we can identify areas where our models can be improved, as well as areas where our models are performing well.
Conclusion
In conclusion, Experiment Tracking and Run History are two powerful features that make it easier to track and compare experiments in Azure Machine Learning. These features can help save time and effort in the process of designing and building models, as well as identifying areas for further exploration. With these features, users can quickly and easily track and compare their experiments, allowing them to quickly identify areas for improvement and save time and effort in the process.
