Best MLOps Practices for AI-Driven Startups

Somatirtha

Version datasets, code, and models to guarantee reproducibility and faster production rollbacks always.

Integrate CI/CD pipelines to automate testing, validation, and seamless model deployment workflows efficiently.

Track experiments continuously to compare runs, optimize hyperparameters, and improve collaborative development speed.

Monitor production for data drift, latency, accuracy drops, and business impact metrics proactively.

Adopt feature stores to ensure consistent training-serving data and eliminate duplicated engineering effort.

Use managed cloud infrastructure to scale rapidly without building complex platforms from scratch.

Align machine learning outputs directly with product goals to deliver measurable customer value.

Implement governance frameworks capturing metadata, dependencies, and lineage for compliance and audit readiness.

Optimize costs through autoscaling, efficient inference, and continuous performance benchmarking across environments.

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