Open-source technologies have had a profound impact on the world of AI and machine learning, enabling developers, data scientists, and organizations to collaborate, innovate, and build better AI solutions. At Azure Open Source Day, we highlighted Microsoft’s commitment to open source and how to build intelligent apps faster and with more flexibility using the latest open-source technologies that are available in Azure AI.
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The growing adoption of data-driven and machine learning-based solutions is driving the need for businesses to handle growing workloads, exposing them to extra levels of complexities and vulnerabilities. Here are some key approaches and tests for securing your machine learning systems against attacks with Azure Machine Learning using MLOps.
The capacity of a system to adjust to changes by adding or removing resources to meet demand is known as scalability. Here are some tests to check the scalability of your MLOps model.
Robustness is the ability of a closed-loop system to tolerate perturbations or anomalies while system parameters are varied over a wide range. There are three essential tests to ensure that the machine learning system is robust in the production environments: unit tests, data and model testing, and integration testing.
Testing is an important exercise in the life cycle of developing a machine learning system to assure high-quality operations. In this blog, we will look at testing machine learning systems from a Machine Learning Operations (MLOps) perspective and learn about good case practices and a testing framework that you can use to build robust, scalable, and secure machine learning systems.