87% of AI projects fail in production (Gartner). MLOps (Machine Learning Operations) is the discipline that reduces this rate by applying DevOps principles to the full AI model lifecycle.
Every dataset or model change is tracked (DVC, MLflow, W&B). Reproducibility guaranteed: a production model can be exactly recreated from its hash.
Automated pipeline: commit → training → evaluation → validation → deployment. Quality thresholds (accuracy, latency, bias) block deployment if not met.
Centralised repository of shared features (Feast, Tecton, Vertex AI Feature Store). Prevents transformation replication. Guarantees consistency between training and inference.
Continuous monitoring of input data distributions (data drift) and model performance (model/concept drift). Tools: Evidently AI, Arize, WhyLabs.
Progressive deployment: shadow mode, canary release (5% traffic), blue/green deployment. Automatic rollback if metrics degrade.
Apache Airflow, Kubeflow Pipelines, Prefect, ZenML. Kubernetes as universal runtime for isolation and scalability.
MLflow (open-source, on-premise deployable), Weights & Biases, Neptune.ai. Tracks parameters, metrics, artefacts, environment.
Triton Inference Server (NVIDIA), BentoML, Seldon Core, Ray Serve. Optimisations: INT8 quantisation, dynamic batching, semantic caching.
Vertex AI, Azure ML, SageMaker, Databricks MLflow. Cost vs control: managed platforms ×3 cost but ×10 faster start.
Molderez Consult SRL supports AI technology integration into your systems.
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