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Machine learning operations (MLOps) technology and practices enable IT teams to deploy, monitor, manage, and govern machine learning projects in production. Much like DevOps for software, MLOps provides the tools you need to maintain dynamic machine learning-driven applications. The security of your future enterprise depends on the decisions you make today related to these new applications and the code that powers them. So, what are the risks?
Good People, Bad Code – Data scientists are known for building predictive models and not for their coding skills. Taking their handwritten code and putting it straight into production is a recipe for failure and a potential security risk.
Malicious Code – If someone wanted to harm your business, introducing code into your production machine learning applications would be one way to cause problems. This problem is compounded when your data science team uses a language like Python or R that your IT team doesn’t understand, making it so that your IT team cannot review the code. This code could return bogus results or overload servers and create any number of issues. Malicious code is most likely to work if you don’t have a proactive way to know if production models and their …
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