At Datasaur, we've got you covered when it comes to data labeling. We understand the importance of being able to integrate with various machine learning platforms, including Azure. We recognize that Azure is a powerful tool for building and deploying ML models, and that's why we've prioritized seamless integration with it. Our mission is to empower data scientists and machine learning engineers to build and deploy highly accurate and effective models on any platform they choose.
In this tutorial, we will take you through the process of building a model with Azure integration. This tutorial is meant to be practical and easy to follow, even for beginners. It is a step-by-step guide that will walk you through the entire process of building a model that can be deployed easily on Azure.
We will start with labeling data using Datasaur, then move on to building a model with Azure Automated ML. We will provide a detailed guide with screenshots to ensure that you have a smooth experience throughout the process.
By following this tutorial, you will learn how to:
If you are ready to get started, let's dive in!
a. In this step, you need to add a dataset, ensuring that it includes a split between training and validation data.
b. Once you have set the data split, you can configure the necessary job.
c. Set the training task to classification.
d. You can add additional configurations such as allowed models, training job time, and metrics thresholds. Allowed models are used to list all the model variants that we want to use in the training process. Training job time and metrics threshold define the exit criterion. When this threshold value is reached for an iteration metric, the training job will be terminated. We recommend you use 24 hours and 1 iteration as the value for training job time and maximum iteration.
e. You can also modify the featurization config.
f. You need to also set your validation data.
g. All steps are set! We are ready to see the result of our machine learning training.
6. After completing the necessary steps to train your machine learning model with Azure AutomatedML, you will be able to view your training performance results. This valuable information will give you insight into the accuracy and effectiveness of your model, allowing you to make any necessary improvements or adjustments.
In order to utilize the machine learning model that you have built, it is necessary to deploy it. This involves making the necessary configurations and settings so that the model can be accessed and used by others.
Deploying a model can be a complex process, but with the right guidance and steps, it can be a straightforward task. Below are the steps you can follow to deploy your model:
By using your ML model, you can efficiently obtain labeled data, which can save you time and effort in the data preparation process. This is because your model can use its predictions to label data automatically. For example, if you're building a model to predict whether an email is spam or not, you can use your model to label a large dataset of emails automatically. This can be more efficient than manually labeling each email, which could take a significant amount of time.
Datasaur's flexible annotation capabilities enable data scientists to annotate data in a way that suits their specific use case. This can include multiple annotation workflows, such as single annotation or consensus annotation, ensuring that you can get the most out of your data.
In addition to its flexible annotation capabilities, Datasaur offers seamless integration with Azure. With Azure integration, users can build and deploy their own models in a matter of minutes. Whether you are an experienced data scientist or a beginner just starting with machine learning, Datasaur's integration with Azure makes it easy to get started and achieve your goals.
By using Datasaur to label your data and then training your machine learning model with Azure AutomatedML, you can create highly accurate and effective models that can be deployed on any platform of your choice within minutes! Datasaur's assisted labeling feature also allows for efficient and accurate labeling, saving you time and effort in the data preparation process.
Overall, Datasaur's integration with Azure provides a powerful and efficient solution for data labeling and machine learning model training. Whether you are working on a small-scale or large-scale project, Datasaur's capabilities and integration with Azure can help you achieve your goals quickly and easily.
