How Doppel eliminated ML infrastructure tax with Modal

Doppel, an AI-native cybersecurity platform focused on detecting and disrupting social engineering attacks, migrated much of its machine learning workflow to Modal to accelerate experimentation and simplify real-time model deployment. Previously, sequential training runs, lengthy jobs, costly failures, and bundled changes slowed experimentation, while Modal enabled parallel execution of independent tasks such as cross-validation through standard Python constructs, improving the speed of evaluating hypotheses. The company also uses coding agents for operational tasks including launching experiments, gathering metrics, and summarizing outcomes, while machine learning engineers retain responsibility for selecting worthwhile ideas. For inference, Doppel moved away from a GCP Cloud Run workflow involving custom Docker containers, single-GPU instances, Flask endpoints, and potentially slow builds or scaling delays. Modal’s image caching, persistent model-weight volumes, serverless scaling, and direct function invocation reduced deployment times, integration code, and infrastructure management, allowing the team to focus more on developing and assessing detection models.

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