Accurate machine learning (ML) models are fundamental to the success of modern businesses and technologies. From predicting consumer behavior and automating complex decision-making to enhancing medical diagnostics and driving autonomous vehicles, ML’s applications are vast and transformative.

Precise models empower organizations to make informed decisions, optimize operations, and innovate in ways responsive to dynamic market conditions and customer needs.

A common issue in machine learning is that while models are trained on high-quality data, this data may not represent real-world scenarios. Model decay refers to the degradation in a model’s performance over time. This decline in predictive accuracy can be attributed to several factors that cause degradation.

Data is unpredictable, and developers need to understand and track changes as they affect the predictions of the models. A model monitoring pipeline helps detect and correct prediction quality deviations and triggers automated training if signs of model decay are observed.

This paper discusses the importance of continuous monitoring of ML models in production, focusing on model decay caused by training-serving skew, data drift, model drift, and error accumulation.

What is Continuous ML Model Monitoring?

Machine learning (ML) model monitoring is the process of tracking and evaluating the performance of an ML model once it’s deployed in a production environment. This ongoing supervision is essential to ensure that the model continues to perform as expected over time and to detect any issues that might arise after deployment. Here are the key aspects of ML model monitoring:

• • Data Drift: Data drift occurs when the statistical properties of the input data change over time (gradually), which might not be reflected in the training dataset. Monitoring for data drift helps identify when the model might start to perform poorly because the input data no longer represents the conditions under which the model was trained.
  • Model Drift: Model drift, also known as concept drift, is when the statistical properties of the target variable, which the model tries to predict, change over time. This can be due to changes in external factors affecting the data or evolving patterns.
  • Training-Serving Skew: This refers to the mismatch between an ML model’s performance during its training phase and its performance when it’s actually used (served) in production. Training Skews arise from pipeline discrepancies in handling data, data shifts, feedback loop amplification, etc.
  • Feedback Loops: Implementing feedback mechanisms allows real-time adjustment and recalibration of the model based on new data and outcomes, helping the model to adapt over time.
  • Performance Metrics: Continuous monitoring measures performance metrics like accuracy, precision, recall, and F1-score. These metrics can help detect any degradation in the model’s performance.

Effective model monitoring can alert developers and data scientists to performance issues, which could stem from various causes, such as changes in input data, model degradation, or operational anomalies. This allows timely interventions to maintain the accuracy and reliability of machine learning systems in production.

ML model monitoring is performed over the inference phase. Inference is the process where a trained machine learning model is used to make predictions. It is distinct from the training phase, where the model learns from examples.

You can run predictions on a batch of new data (batch predictions) for apps that don’t require real-time results. Or you can run online predictions on real-time data for apps that require immediate decision-making. With online predictions, you deploy them to an endpoint that accepts predictions and codes the logic.

ML Model Monitoring Pipeline

One approach to model monitoring is to periodically make batch predictions with new data and evaluate the model’s accuracy. If the current accuracy falls short of the initial training, it indicates a potential problem.

AI engineers create custom code to build a continuous monitoring pipeline or configure automated monitoring services that operate regularly, such as daily or weekly.

A model monitoring pipeline distinct from training and serving pipelines runs independently and can run in parallel or after the pipeline.

It’s crucial to store the predictions made during serving—whether online or in batch form—in a structured format, such as a database or cloud bucket, to feed the ML monitoring pipeline.

An ML model monitoring pipeline is designed to continuously track the health and performance of an ML model deployed in production. Here’s a breakdown of how it typically works:

Data Collection:

• • The pipeline gathers data from various sources related to the model’s performance. This can include:
    • Model Predictions: The actual outputs generated by the model for new data points.
    • Ground Truth: If available, the true values for the predictions are used to calculate performance metrics.
    • Input Data Features: The characteristics of the data being fed to the model.
    • Model Metrics: Predefined performance measures like accuracy, precision, recall, etc.
    • System Health Checks: Monitors for issues in the data pipelines or the model serving infrastructure.

Data Processing and Analysis:

• • The collected data goes through processing and analysis steps:
    • Data Validation: Checks for data quality issues like missing values or inconsistencies.
    • Feature Engineering: This may involve transformations to prepare the data for analysis.
    • Metric Calculation: Calculates relevant performance metrics based on the chosen model evaluation criteria.
    • Drift Detection: Analyze changes in the data distribution (drift) that might affect model performance.
    • Alert Generation: Triggers alerts if metrics fall outside predefined thresholds or drift exceeds acceptable levels.

Alerting and Visualization:

• • The pipeline generates alerts when it detects potential issues:
    • Performance Degradation: Alerts if metrics like accuracy or precision fall below acceptable thresholds.
    • Data Drift: Signals significant changes in the data distribution that could impact model generalizability.
    • System Issues: Alerts for problems in the data pipelines or model serving infrastructure.
  • The pipeline also generates visualizations to help users understand model behavior:
    • Model Performance Trends: Tracks changes in metrics over time to identify potential performance degradation.
    • Data Distribution Shifts: Visualizes changes in the input data features to understand drift.
    • Explainability Tools: Techniques like LIME or SHAP might be used to explain individual model predictions.

Human Intervention:

• • When alerts are triggered, or visualizations indicate issues, human intervention is necessary:
    • Investigate Root Cause: Data scientists and engineers investigate the cause of the alert, be it data drift, training-serving skew, or infrastructure problems.
    • Take Corrective Actions: Actions include retraining the model, adjusting data pipelines, or deploying new models altogether.

Benefits of an ML Model Monitoring Pipeline:

• • Proactive Problem Detection: Catches issues early before they significantly impact model performance.
  • Improved Model Performance: Enables continuous monitoring and adjustments to maintain optimal model effectiveness.
  • Increased Trust and Transparency: Provides evidence of model behavior and helps build trust in its outputs.

By implementing an, you can ensure your models stay healthy and deliver reliable results in production.

Tools for ML Model Monitoring

Dedicated ML Model Monitoring Tools

• • Arize AI: Offers comprehensive model monitoring, including data drift, concept drift, model performance degradation, and explainability.
  • Evidently AI: Provides data quality checks, model performance evaluation, and data drift detection.
  • Fiddler AI: Focuses on model explainability and monitoring, helping to understand model decisions.

MLOps Platforms with Strong Monitoring Capabilities

• • MLflow: While primarily an ML lifecycle management platform, it offers robust experiment tracking and model registry features for monitoring.
  • Kubeflow: Built on Kubernetes, it provides a platform for deploying ML pipelines and models with integrated monitoring capabilities.

Open-Source Options

• • TensorFlow Extended (TFX): Offers a platform for building and managing ML pipelines, including monitoring components.
  • Kubeflow Pipelines: Extends Kubeflow with pipeline orchestration and monitoring capabilities.

Machine Learning Development and Consulting Services

Krasamo provides services encompassing a broad range of activities necessary for successfully implementing and operating machine learning systems in a business context, offering potential clients a comprehensive solution to harness the power of AI.

• ML Data Strategy Development:
  • Assistance with collecting, curating, exploring, and processing data.
  • Implementation of robust data strategies to effectively scale machine learning projects.
• Feature Engineering Services:
  • Data exploration to understand relationships and extract useful features.
  • Iterative validation and optimization of features to enhance model performance.
• MLOps Implementation:
  • Setting up MLOps practices to manage machine learning projects efficiently.
  • Continuous integration and deployment of ML models, ensuring high data quality and consistent model performance.
• Infrastructure Setup:
  • Configuration and management of computing and storage infrastructure suitable for ML projects.
  • Building and maintaining data lakes and warehouses for efficient data handling.
• Real-Time Data Systems:
  • Development of real-time data collection systems using Pub/Sub messaging architectures.
  • Integration with cloud platforms for streamlined data processing and analytics.
• Data Governance and Compliance:
  • Ensuring data security, privacy, and compliance with relevant regulations.
  • Implementation of data protection strategies like data masking or anonymization.
• End-to-End ML System Design:
  • Comprehensive design and deployment of machine learning systems from data collection to model deployment.
  • Collaboration with domain experts to tailor models to specific business needs.
• Custom ML Model Development:
  • Building bespoke machine learning models tailored to unique business challenges.
  • Experiment with different algorithms and model configurations to find optimal solutions.
• Machine Learning Consulting:
  • Providing expert advice and guidance on best practices in machine learning.
  • Helping clients understand the potential and limitations of machine learning within their specific context.