DevOps engineer to MLOps Engineer – The Ultimate Roadmap

This roadmap focuses on practical knowledge and skill gaps you need to bridge to become a proficient MLOps Engineer:

• Understanding the MLOps Landscape: Clearly differentiate between traditional DevOps and the unique challenges posed by MLOps (e.g., data drift, model decay).
• The MLOps Toolchain: Master the specialized tools required for ML projects, including DVC (Data Version Control), MLflow (Experiment Tracking), and orchestration platforms like Kubeflow or Sagemaker.
• Building ML CI/CD Pipelines: Learn how to create end-to-end automated pipelines that trigger retraining, testing, and deployment based on data or code changes.
• Model Monitoring and Observability: Implement effective strategies for real-time monitoring of model performance and data quality in production environments.
• Reproducibility and Governance: Establish practices for ensuring ML experiment reproducibility and implementing model versioning and governance for regulatory compliance.