MLOps Pipeline Automation - Zero-Downtime Deployments

Published

We’re thrilled to announce enterprise-grade MLOps pipeline automation, enabling teams to deploy and manage machine learning models with confidence. This update brings zero-downtime deployments, automated model validation, and production ML monitoring to your AI infrastructure.

Key Highlights:

  • Automated Training Pipelines: Schedule and execute model training with automatic hyperparameter tuning
  • Model Registry: Version-controlled model storage with lineage tracking
  • A/B Testing Framework: Deploy multiple model variants simultaneously and route traffic based on performance
  • Gradual Rollout: Canary deployments with automatic rollback if performance degrades
  • Drift Detection: Monitor model performance in production and trigger retraining when accuracy drops

Pipeline Stages:

  1. Data Validation: Automated data quality checks and schema validation
  2. Training: Distributed training on Kubernetes with GPU autoscaling
  3. Evaluation: Comprehensive testing on holdout datasets with metrics tracking
  4. Staging: Shadow deployment for pre-production validation
  5. Production: Gradual rollout (5% → 25% → 50% → 100% traffic)
  6. Monitoring: Real-time performance dashboards and alerting

Supported Frameworks:

  • PyTorch, TensorFlow, scikit-learn
  • XGBoost, LightGBM, CatBoost
  • Hugging Face Transformers
  • Custom MLflow and Kubeflow pipelines

This update is available for Enterprise plan customers. Contact our sales team to learn how MLOps automation can streamline your ML operations.