Co-Founder Taliferro
Introduction
Experiments are the primary unit of work in Azure Machine Learning Studio. They allow you to track different versions of your machine-learning models and determine which is best for your business needs. Experiments are isolated and designed to test specific parameters or models. Experiments save time and energy, reduce risk and give data-driven insights for better decision-making.
What is a machine learning experiment
In Azure Machine Learning, an experiment is the primary unit of work. An experiment is designed to track different versions of your machine-learning model and provide a way to perform validation tests on each version independently.
Experiments are isolated from each other so that they can run in parallel. This isolation prevents one experiment from affecting another—for example, if one experiment is running a long-running process like training a model or generating predictions on new data, it should not affect how quickly another experiment runs its processes.
Experiments using the SDK
Experiments are a way to test different versions of your machine-learning models on the same data set.
Experiments can be run in parallel, so they're great for maximizing the speed of your iterative development process. Experiments allow you to test different parameters and configurations in one place rather than creating separate training jobs for each model configuration. This enables you to quickly evaluate how various model configurations perform on real-world data sets without having to write code or deploy new models whenever you want to try something different with your ML workloads.
As with all experiments, you must run this test with a large enough sample size to be confident in the outcome.
If you've done any data science work before, you might recognize this as a "train-test split." You'll want to set aside some portion of your dataset for testing purposes; that way, we're not just using our intuition when deciding whether or not one model is better than another.
Experiments are isolated and designed to test specific parameters or models. This is an important distinction because it means that the results of one experiment won't affect another by making assumptions that aren't true.
Experiments should be designed to be repeatable and easily reproduced so that they can be run in parallel with other experiments on different computers.
Experiments are usually designed to run in a single machine (or cluster) rather than distributed across multiple devices, but this is only sometimes the case. Some experiments may require local access to input data or output files from previous experiments. In these cases, you'll need to design your experiment's dependencies carefully so that you don't waste resources running unnecessary steps every time someone runs your code.
You can use experiment results to determine the best machine learning model. The process of doing so is called model selection, or finding the best fit for a specific problem and dataset.
Experiments save time and energy by allowing us to do more with fewer resources while reducing risk by isolating our code from production environments. Data scientists can use experiments to automate their workflows, giving them more time for data exploration and creative thinking, leading to better decision-making about ML strategies.
Experiments in Azure Machine Learning Studio
Experiments are the primary unit of work in Azure Machine Learning Studio. They use a versioned code repository and can be run locally, on your hardware, or in the cloud. Experiments are isolated and designed to test specific parameters or models, enabling you to determine the best machine-learning model quickly.
Conclusion
Experiments are a crucial part of machine learning models. They can help you refine your models by providing data-driven insights into what works best for your particular dataset and use case. I encourage you to try Experimentation in Azure Machine Learning Studio, where it's easy to create and manage experiments with one click. These are some of the basics of experiments in Azure Machine Learning Studio.
Tyrone Showers