This post is co-written with Mahima Agarwal, Machine Learning Engineer, and Deepak Mettem, Senior Engineering Manager, at VMware Carbon Black
VMware Carbon Black is a renowned security solution offering protection against the full spectrum of modern cyberattacks. With terabytes of data generated by the product, the security analytics team focuses on building machine learning (ML) solutions to surface critical attacks and spotlight emerging threats from noise.
It is critical for the VMware Carbon Black team to design and build a custom end-to-end MLOps pipeline that orchestrates and automates workflows in the ML lifecycle and enables model training, evaluations, and deployments.
There are two main purposes for building this pipeline: support the data scientists for late-stage model development, and surface model predictions in the product by serving models in high volume and in real-time production traffic. Therefore, VMware Carbon Black and AWS chose to build a custom MLOps pipeline using Amazon

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