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張貼者:Takuto Higuchi

MLOps Blog Series Part 4: Testing security of secure machine learning systems using MLOps

2022年7月12日

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.

Product Marketing Manager, Data and AI Marketing

MLOps Blog Series Part 2: Testing robustness of secure machine learning systems using machine learning ops

2022年6月22日

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.

Product Marketing Manager, Data and AI Marketing

MLOps Blog Series Part 1: The art of testing machine learning systems using MLOps

2022年6月14日

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.

Product Marketing Manager, Data and AI Marketing