The Road to MLOps: How Verivox Transitioned to AWS

Plagued by months-long deployment cycles, Verivox transformed their ML workflow. See how they used AWS CDK and MLOps to achieve deployments in just hours.

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Understanding the role and challenges of MLOps

MLOps provides a structured process to build and integrate machine learning products by addressing challenges beyond just the ML code, such as versioning, security, and deployment.

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Navigating the four phases of MLOps maturity

The MLOps maturity model guides teams through four phases: accelerating proof of concept, making processes repeatable, ensuring reliability through monitoring, and achieving scalability.

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Overcoming siloed code and deployment bottlenecks

Verivox's initial setup suffered from siloed codebases, a lack of deployment ownership, and friction between teams, prompting a complete operational transformation.

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Executing a multi-stage initial migration to AWS

The team's first project involved migrating from R to Python and moving from manual UI clicks to a fully automated CI/CD pipeline with infrastructure as code.

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Building a real-time inference architecture on AWS

A standardized blueprint using Amazon SageMaker Pipelines and AWS Lambda was created to solve the major pain point of deploying models for real-time inference.

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Using AWS Fargate for flexible batch processing

A container-based architecture with AWS Fargate and Step Functions provides the flexibility needed for custom batch jobs and lifting-and-shifting legacy projects.

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Automating infrastructure with AWS CDK templates

AWS Cloud Development Kit (CDK) enables the creation of reusable, parameterizable infrastructure templates to scale deployments across multiple projects, accounts, and sandboxes.

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Key learnings and results from the MLOps transformation

The migration resulted in drastically reduced deployment times, improved reliability, and new capabilities, underscoring the value of support networks and managed services.