We are excited to announce the Public Preview of automated ML (AutoML) for Images within Azure Machine Learning (Azure ML). This new capability boosts data scientist productivity when building computer vision models for tasks such as image classification, object detection and instance segmentation.
Customers across various industries are looking to leverage machine learning to build models that can process image data. Applications range from image classification of fashion photos to PPE detection in industrial environments. The ideal solution will allow users to easily build models, control the model training to optimize model performance, and offer a way to easily manage these ML models end-to-end. While Azure Machine Learning offers a solution for managing the end-to-end ML lifecycle, customers have traditionally had to rely on the tedious process of custom training their image models. Iteratively finding the right set of model algorithms and hyperparameters for these scenarios typically requires significant data scientist effort.
With AutoML support for computer vision tasks, Azure ML customers can now easily build models trained on image data, without writing any training code. Customers can seamlessly integrate with Azure ML's data labeling capability and use this labeled data for generating image models. They can control the model generated, selecting from a variety of state of the art algorithms and can optionally tune the hyperparameters to optimize model performance. The resulting model can then be deployed as a web service in Azure ML or downloaded for local use and can be operationalized at scale by leveraging Azure ML’s MLOps capabilities.
Authoring AutoML models for computer vision tasks is currently supported via the Azure ML Python SDK. The resulting experimentation runs, models and outputs are accessible from the Azure ML Studio.
Following is a summary of features and benefits of AutoML for Images -
Use Azure ML data labeling to manage labeling your image data. Co-ordinate data, labels and team members to efficiently manage labeling tasks. Export the labeled data to an Azure ML Dataset, that can be used to train your computer vision model.
You can either specify a single model algorithm or explore and compare multiple algorithms in a single AutoML run.
You can optionally sweep across multiple algorithms and hyperparameters in a single AutoML run, to find the optimal settings for your model. This feature applies the hyperparameter tuning capabilities in Azure Machine Learning, allowing you to control sampling methods, early termination policies and resources spent on the sweep. A sample configuration showing how to leverage this capability is included below -
In summary, you can use AutoML for Images to easily build and optimize computer vision models, while offering flexibility and control over the entire model training and deployment process. Please give it a try and share your feedback with us.
You must be a registered user to add a comment. If you've already registered, sign in. Otherwise, register and sign in.