The provided text presents a detailed interview with a lead MLOps engineer, exploring their practical experience in deploying and managing machine learning models in production.
The candidate elaborates on specific projects, including deploying models on AWS SageMaker using tools like Docker and Kubernetes.
They discuss model optimization techniques, such as quantization, and detail their approach to balancing size and accuracy with measurable results.
The interview also covers the candidate's expertise in establishing robust CI/CD pipelines using Jenkins and Argo CD, along with strategies for monitoring, scaling, and ensuring the security of deployed models.
Furthermore, the candidate explains their methods for detecting model drift using Evidently AI and managing version control with MLflow and DVC.
Finally, the discussion includes a real-world incident, showcasing their problem-solving skills and commitment to continuous improvement in MLOps practices.
⚙️MLOps Expertise
The article presents a detailed interview with a lead MLOps engineer, exploring their practical experience in deploying and managing machine learning models in production.
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