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MLOps Lifecycle: From Model to Production and Beyond

1. Introduction: Operationalizing Machine Learning​

Machine Learning models are no longer experimental features; they are core components of the XOPS platform, powering Cerebro's intelligence and Sparky's decision-making. Machine Learning Operations (MLOps) is the discipline that ensures our ML models are developed, deployed, monitored, and managed reliably and efficiently throughout their lifecycle.

This document outlines our MLOps strategy, focusing on the processes and tools we use to manage models from experimentation to production. It emphasizes automation, reproducibility, and continuous improvement.

Core Mission: To reliably deploy and maintain machine learning models in production, ensuring they deliver continuous business value and adapt to changing data and environments.

2. The MLOps Lifecycle​

Our MLOps lifecycle is a continuous, iterative process that mirrors traditional DevOps but with specific considerations for ML.

  1. Experimentation & Development: Data scientists and ML engineers explore datasets, develop hypotheses, and experiment with model architectures. This phase uses tools like AWS SageMaker Studio Notebooks.
  2. Model Training: Once a promising model is developed, it's trained on curated datasets. This is an automated process, often using SageMaker Training Jobs for scalability and reproducibility.
  3. Model Evaluation: Trained models are rigorously evaluated against key performance metrics (e.g., accuracy, precision, recall, F1-score) and business KPIs. This includes checking for bias and fairness.
  4. Model Registration: Approved models are registered in an ML Model Registry (e.g., SageMaker Model Registry). Each registered model has a version, lineage, and evaluation metrics.
  5. Model Deployment: Registered models are deployed as scalable endpoints using SageMaker Endpoints. We use canary deployments for new model versions.
  6. Model Monitoring: Once deployed, models are continuously monitored for performance degradation, data drift, and concept drift.
  7. Retraining & Redeployment: Based on monitoring feedback, models are retrained with new data and redeployed.

3. Model Training Pipelines​

Automated, reproducible training pipelines are critical for MLOps.

  • Framework: We use AWS SageMaker Pipelines to orchestrate our training workflows.
  • Components of a Pipeline:
    • Data Preparation: Scripts to fetch data from the Knowledge Graph, perform transformations, and store it in an S3 bucket. This step is versioned.
    • Training Script: The core ML code (e.g., Python script using TensorFlow/PyTorch) that trains the model.
    • Hyperparameter Tuning: Automated search for optimal model hyperparameters.
    • Model Evaluation: Scripts to compute evaluation metrics.
    • Model Registration: If evaluation metrics meet the bar, the trained model artifact is registered in SageMaker Model Registry.
  • Triggering: Training pipelines can be triggered manually, on a schedule (e.g., weekly retraining), or automatically when new data becomes available in the Knowledge Graph.
  • Reproducibility: Every pipeline run is logged, and all code and data versions are tracked, ensuring that a specific model can be reproduced.

4. Model Deployment Strategies​

Deploying models requires careful consideration to minimize risk.

  • SageMaker Endpoints: Models are deployed as real-time, managed endpoints.
  • Canary Deployments: We roll out new model versions to a small percentage of traffic first. If performance and accuracy remain high, we gradually roll out to 100%. If issues arise, we automatically roll back to the previous stable version. This is orchestrated by SageMaker Endpoints configurations and potentially automated by Sparky based on New Relic alerts.
  • A/B Testing: For evaluating new model versions side-by-side, we can deploy two versions to separate endpoints and direct traffic to both, analyzing results to pick the winner.

5. Model Monitoring: Detecting Drift and Degradation​

Production models are not "set and forget." Continuous monitoring is vital.

  • Performance Monitoring:
  • Data Drift:
    • What it is: The statistical properties of the input data to the model have changed compared to the training data.
    • Monitoring: SageMaker Model Monitor compares live inference data to the baseline data used for training.
    • Action: If significant drift is detected, an alert is triggered, prompting investigation and potential retraining.
  • Concept Drift:
    • What it is: The relationship between the input features and the target variable has changed. The model's underlying assumptions are no longer valid.
    • Monitoring: This is harder to detect automatically. We rely on monitoring for unexpected drops in model accuracy/business KPIs and proactive retraining.
    • Action: Trigger a retraining pipeline with fresh labeled data.
  • Alerting: Alerts for monitoring failures are sent to PagerDuty and analyzed by Sparky.

6. Feature Store​

A centralized Feature Store is essential for ensuring consistency between features used during training and inference, and for promoting feature reuse across multiple models.

  • Purpose: To store, discover, and serve ML features.
  • Technology: We utilize AWS SageMaker Feature Store.
  • Usage: ML engineers define features, compute them, and register them in the Feature Store. When training a model or deploying it for inference, the pipeline or endpoint retrieves features from the store, guaranteeing that the same feature engineering logic is applied in both environments.

7. Integration with Other Tools​

  • AWS SageMaker: The core platform for training, deployment, and monitoring.
  • Knowledge Graph: The primary source of historical data for training and real-time data for inference.
  • New Relic/CloudWatch: For infrastructure and endpoint performance monitoring.
  • Sentry: To capture exceptions related to model serving.
  • Jira Service Management: For tracking issues related to model performance degradation or retraining needs.
  • GitHub Actions: To orchestrate training pipelines and model deployments.
  • Sparky/Cerebro: To act on monitoring alerts and trigger retraining workflows.

This comprehensive MLOps strategy ensures our AI capabilities remain robust, reliable, and valuable over time.