The most advanced ML Observability platform
Building an ML platform is nothing like putting together Ikea furniture; obviously, Ikea is way more difficult. However, they both, similarly, include many different parts that help create value when put together. As every organization sets out on a unique path to building its own machine learning platform, taking on the project of building a […]
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We’re excited 😁 to share that Forbes has named Aporia a Next Billion-Dollar Company. This recognition comes on the heels of our recent $25 million Series A funding and is a huge testament that Aporia’s mission and the need for trust in AI are more relevant than ever. We are very proud to be listed […]
Azure MLOps enables organizations to adopt and manage machine learning operations (MLOps) pipelines in the Azure cloud. Microsoft provides Azure Machine Learning (Azure ML) as its primary solution for practicing MLOps in Azure, but there are other ways to create ML pipelines in the Azure cloud.
Azure Machine Learning lets you bring your own models built using popular ML frameworks like TensorFlow, Pytorch, or scikit-learn.
ML professionals, engineers, and data scientists can use Azure Machine Learning to manage their ML workflows, including training, deploying, basic monitoring, and retraining production models.
Here are key features of Azure Machine Learning:
Azure Machine Learning offers many MLOps capabilities. It enables you to create reproducible ML pipelines that define reusable and repeatable steps for data preparation and training. You can build a reusable software environment to train and deploy ML models, and package, register, and deploy ML models from any location.
Azure Machine Learning lets you track metadata, capture governance information for your ML lifecycle, and include various information in the logged lineage information, such as the model’s publisher models, changes justification, and when the model was deployed and used in the production environment.
You can use Azure Pipelines and Azure Machine Learning to automate the entire ML lifecycle. Pipelines help you frequently update existing models, test new ML models, and periodically roll out new models with your services and applications.
You can track the entire audit trail of all ML assets using metadata. Azure Machine Learning can integrate with Git to help you track information on the repositories, branches, and commits certain code originates from. You can employ Azure Machine Learning datasets to profile, version, and track data.
You can leverage this interpretability to explain ML models, understand how your models reach the result for a specific input, and ensure regulatory compliance. Additionally, Machine Learning job history can store a snapshot of all data, computes, and code you use to train your model. Machine Learning Model Registry can capture all metadata related to ML models.
The image below includes a reference architecture. It shows how you can implement continuous integration and continuous delivery (CI/CD) and retrain a pipeline for an artificial intelligence (AI) application with Azure DevOps and Azure Machine Learning.
Azure built this example solution on the scikit-learn diabetes dataset. However, you can adapt it for other AI scenarios and build systems like Travis and Jenkins.
The build pipelines in the image include DevOps tasks for:
The architecture includes the following services:
Here are best practices to follow when setting up MLOps with Azure Machine Learning:
Aporia is a natively supported monitoring solution for machine learning applications running on Azure. It can be seamlessly integrated into the end of your pipeline to provide real-time monitoring and tracking of your ML processes.
Aporia’s ML monitoring solution can be used in conjunction with Azure MLOps to provide additional capabilities for monitoring and managing machine learning models in production.
There are several reasons why an organization using Azure could benefit from integrating Aporia’s ML monitoring solution into their machine learning workflow:
Overall, integrating Aporia with Azure MLOps can help organizations gain a more comprehensive view of their machine learning models and showcase their performance in production, helping to drive better outcomes and a better overall user experience.
To get a hands-on feel for Aporia’s ML Observability Platform, we recommend: