Model Deployment Architecture Assessment

Are your models ready for deployment into production?

In a one-on-one setting, we will apply our experience to your circumstances using our enterprise-ready MLOps reference architecture that will provide the knowledge and roadmap you need to succeed.   Learn how to avoid common deployment problems and how to plan a strategy for MLOps including:
  • Deploying ML applications with agility by using CI/CD and other automation within an MLOps framework
  • Monitoring for degradation of model accuracy and data drift, not just application availability
  • Using model registries and feature stores to facilitate traceability, governance, and make future models easier to build by building upon prior work
In a free one-hour consultation, Dominick Rocco will talk through how your process compares with best practices, potential problems and their recommended solutions, and any other actionable recommendations for success.

What will you get?

With over a decade of experience in data science and machine learning, Dominick is an expert in designing repeatable, explainable, and observable ML systems. Throughout his career, he has developed and operationalized ML systems across many domains – from health care to particle physics and beyond. Dominick is passionate about solving complex problems in complex environments and figuring out the best way to establish architectures that will scale with your solution.
Dominick Rocco Headshot

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