What tools can you use to monitor the performance of an ML model in real-world scenarios?

Unlike traditional software, failure in ML model is subtle and may not be obvious immediately. Your system won't stop working all of a sudden but gradually. You'll start loosing customer engagement, the efficiency of the machine you are monitoring will decline gradually and so on. Since there is no obvious/immediate failure, if not monitored continuously, these problems are caught late and by then a significant damage might have been done. That's why ML model should be monitored proactively and we should not wait for signs of failure to take an action.

What ML people call monitoring, in systems software we call observability. There are 2 components to observability: * logging * measuring To solve logging and measuring, we need a single "logging pipeline" (to add to our feature/training/inference pipelines). The "logging pipeline" will be logically the same for batch and real-time ML. It will store rows containing a prediction and its feature inputs (unencoded). Data scientists can use the logged table of predictions for (1) debugging - find those outlier predicitions to understand why your model predicted as it did, and (2) monitoring - compute statistics over a detection window using predictions logs and compare them with a reference window (the training data set).

From my experience, monitoring plays a pivotal role in delivering top-tier ML-powered services on an enterprise scale. With tech companies wholeheartedly embracing AI-driven strategies, establishing robust systems for ML model oversight is a significant challenge. Monitoring provides quick access to incident data, allowing real-time responses like alerts or immediate actions, which is pivotal. Monitoring reveals significant shifts in data and its distributions that can substantially impact predictive performance, a phenomenon known as "drift." Drift detection is a proactive system, ensuring your ML-driven decisions stay on the right path. Moreover, monitoring builds transparency and trust, which is crucial in AI and ML.

Proactive data level monitoring falls more in the observability domain. You can use OpenTelemetry to monitor model data drift and degradation. The OpenTelemetry traces can be captured by monitoring dashboards like Dynatrace and Jaeger. The deployment errors can be directly captured by the same dashboards. Metrics attached to this captured data traces can instantly flag violation for inidividual data packets or a drift over time. The beauty of this method is that it minimizes the amount of coding, and leverages standard tooling for the most part. Just follow structured tracing emitted by using OTEL libraries and then tools to capture and dashboard and attach metrics. The coding involved is comparable to writing logfile messages.

Machine learning solutions for businesses should always be incremental in nature - a good enough model in production is more valuable than a perfect model still in development. Monitoring ML model is that crucial feedback link which allows us to learn from experience and subsequently improve the model & related processes.

For optimal results, I recommend cross-referencing model performance using various methods: 1. AUC/ROC Curve AUC ranges from 0 to 1, with 0.5 indicating random performance and 1.0 signifying perfect separation. 2. K-S Test Compute the CDFs of both distributions and determine the maximum difference, termed the K-S statistic or score, which can vary from 0 to 1. 3. Gini Index Linked to the ROC/AUC score, the Gini Index evaluates based on historical data. It weighs all parameters, reflecting their impact on decisions. Note: A Gini score of 0 corresponds to an ROC AUC score of 0.5.

Know your why (covered above). From my experience, monitoring enterprise-scale ML-powered services is no small task. Monitoring is important in ensuring top-notch model performance which relies on critical performance metrics like accuracy, drift, anomaly detection, etc. Tracking anomalies and outliers is pivotal, as they can disrupt model predictions significantly. Then monitoring for drift is crucial to detect when your data distribution diverges from the training data. This is a key signal, indicating when to retrain the model. And last but not least, monitoring for explainability is important. As it provides insights into the factors influencing predictions. This is vital to fostering trust and understanding in ML-services.

Some key elements in ML model monitoring based on my experience: Data Quality: Monitor for quality and inconsistency issues like missing values and outliers. Data Drift: Compare new data with the train data that the model 'learned' from, triggering alerts for significant differences and the potential need for retraining. Model Performance: Check model performance in predicting its target, measured by relevant metrics such as accuracy, precision and recall. Model 'Reasoning': any change in the model's decision-making process and the key features that shape model predictions. Model Implementation: Track how stakeholders use the model. e.g., any human overrides? If yes, analyze the performance of and reasons behind overrides.

Model monitoring is indispensable for enhancing model quality. There are primarily two kinds of monitoring: functional monitoring and operational monitoring. Functional monitoring deals with gauging model performance in its inputs, outputs, and modelling capabilities. Basically, these metrics monitor and regulate the performance at the input data, the model and the predicted output level. Some such metrics detect and regulate: Data- Quality, Drift, and Outliers, Model- Model drifts, Versions and security concerns and predictions. Operational modelling basically monitors the model’s resources and system health in production. The MLOps infrastructure monitors ML pipeline health, performance metrics, and cost.

Monitoring ML models is not just a best practice; it's a crucial aspect of deploying and maintaining machine learning systems. Data quality monitoring is fundamental because the old saying "garbage in, garbage out" holds true for ML. If the data used to train and test your model isn't reliable, the model's predictions won't be either. Model impact tracking gets to the heart of why we build ML models. We want them to make a positive difference, whether it's optimizing ad campaigns or diagnosing diseases. Measuring their impact through key performance indicators is like assessing the ROI of your investment. In summary, monitoring ML models is a multidimensional task that encompasses data quality, model performance, behavior, and impact.

Cover all key elements of a comprehensive monitoring system (discussed above). Consider developing in-house Python libraries to tailor to the teams' best practices and increase efficiency. Tools like Dash allow us to create customized interactive monitoring dashboards with codes easily sharable across projects. Provide both snapshots and time series. From the monitoring dashboards, users should easily distinguish between one-off events and gradual trends. Set up meaningful thresholds for alerts: These thresholds should be designed to avoid missing important signals while minimizing false positives. Log, version and monitor both the model in production and challenger models/rules, ensuring smooth transition whenever necessary.

To effectively monitor ML models, consider using a combination of tools and methods tailored to your needs: Dashboards: Utilize graphical interfaces that display key metrics, charts, or alerts related to your ML models, providing a real-time overview of their performance. Logs: Maintain records of events, actions, or errors during training or inference, enabling the tracking and auditing of model behavior and pipeline activity. Alerts: Set up notifications to promptly identify significant changes, anomalies, or issues in your ML models or their data sources, ensuring timely response.

Deployment | V 2. Data Source Integration (e.g., cameras) | V 3. Model Monitoring ---> TensorBoard (for model metrics) | | | V | Real-time alerts (for performance issues) | V 4. System Health Monitoring --> Prometheus & Grafana (for system metrics) | | | V | Real-time alerts (for system issues) | V 5. Data Drift Detection --> MLflow (detects changes in incoming data vs. training data) | V 6. Feedback Mechanism (User validations) | V 7. Periodic Model Evaluations (to update and refine model)

This is cool tool i used . Evidently helps track ML model quality with built-in standard metrics, checks, and visualizations. You can run pre-deployment model testing, monitor them in production and troubleshoot when things go wrong.

The two best metrics that I have used to monitor ML models are the Population Stability Index and Characteristic Analysis. Both are a way to gauge data shift to estimate if your ML model will be useful or not

You can possibly look at the below tools that provide a diversified toolkit for efficient ML monitoring across varied environments and requirements. Weights & Biases (W&B): A platform for experiment tracking, model optimization, and dataset versioning. Neptune.ai: Offers experiment tracking, model logs, and performance visualization. Evidently: Focuses on data drift detection and model performance visualization. NVIDIA DCGM: Monitors and manages GPUs, useful for deep learning model deployments. Skater: Python library for model interpretation and visualization. Datadog: Infrastructure monitoring that can be adapted for ML models. Kubeflow: Kubernetes-native platform for deploying, monitoring, and operating ML workloads.

ML monitoring tool depends on your specific needs and the nature of your ML projects. It's crucial to assess your monitoring requirements, like performance metrics, bias detection or interpretability and then select the tool that aligns best with those needs.

One can use Vetiver framework developed in R, its a package which helps you to follow a model lifecycle while keeping the insights in handy as when you need it and keeping things handy in place for one to use it with access. The goal of vetiver is to provide fluent tooling to version, deploy, and monitor a trained model. Its easier to, collect manipulate and refine data but lack of effective tools to, - version and publish models - put models into production - monitor model performance Functions handle both recording and checking the model’s input data prototype, and predicting from a remote API endpoint. Feel free to reach out if you are facing issues using it.

There are many but some examples are: 1. W&B (weights and Biases) - helps with training and hyperparameter tuning and enterprise version provides ML monitoring in production. 2. Amazon SageMaker Model Monitor - give variety of features to monitor data and model drifts. Create alerts for incorrect predictions. Some others with major (but not all) features are neptune.ai , Arize Ai, WhyLabs. Compare the cost, enterprise license and the features each tool offers and what your system requires before settling for any one.

ML monitoring tools are essential in maintaining and enhancing model performance. TensorBoard and MLflow cater to those using TensorFlow, offering visualization, management and deployment capabilities.

Select the right ML monitoring tool by assessing its compatibility with your project, evaluating its monitoring capabilities, checking for customization options, and ensuring it meets your budget and compliance requirements.

The choice of tool depends on your specific ML framework, domain, objectives and resource constraints, highlighting the importance of thorough research and testing to select the best fit for your project's needs.

A thorough assessment of these can guide an effective tool selection. Integration & Scalability: Tool's compatibility with current systems. Customizability: Tailor alerts and metrics to unique needs. Security & Compliance: Adherence to standards or regulations. Collaboration: Tools that facilitate team-based work. Support & Community: Evaluate community strength and support responsiveness. Historical Data: Importance of trend analysis. Cost Efficiency: Consider tool cost and potential scaling expenses.

To choose the best machine learning (ML) monitoring tool for your project, consider your project objectives, technology stack, data volume, ease of use, customization, budget, scalability, community and support, regulatory compliance, performance metrics, integration, real-time monitoring, machine learning expertise, data visualization, and feedback mechanisms. The ideal tool aligns with your specific project needs, ensuring effective and efficient monitoring of ML models.

Please include also “engineering” metrics into your monitoring: - how much CPU and RAM is the model consuming? - what’s the latency to return predictions? - what’s the model storage size? This technical things can impact both the user experience and the ROI of your project!

CI/CD and monitoring real-world ML model perf. CI/CD automates the deployment pipeline, ensuring swift integration of model updates. This agility is vital in dynamic envs. where data patterns evolve. By enabling automated testing & deployment, CI/CD facilitates quick release of model improvements. Regular monitoring, a part of CI/CD, evaluates how the model performs in real-time, identifying drifts or issues. Feedback loops allow prompt updates. CI/CD's consistency across dev, test, and deployment ensures that model's behavior in prod is same as it was in testing. Monitoring within CI/CD assures that deployed models align with real-world needs, offering reliable and up-to-date perf. in response to changing data patterns and user behaviors.

In my opinion, "engineering new features" is a crucial component in monitoring ML models. It involves creating additional data attributes that can enhance a model's performance. This process is closely linked to data quality, as these new features must meet standards of validity and completeness. When evaluating model performance, feature engineering can significantly boost accuracy. It also plays a role in understanding model behavior and can have a direct impact on tracking the model's effectiveness in achieving specific objectives. In summary, feature engineering is a powerful tool that improves model quality, performance, behavior, and impact.

Remember that your non technical stakeholders may view monitoring as an “extra”, often because they see ML as a one and done activity. To combat this, building small demos that illustrate how monitoring solutions will make their lives easier as far as understanding the model’s impact and/or using the results can be extremely helpful. “We need to build this so I can track feature importances and manage data drift” probably won’t resonate with a stakeholder concerned about credit card defaults, but monitoring so we can see the effectiveness of the model and proactively identify factors driving defaults (I.e. feature importances) will. Monitoring solutions must speak the language of your stakeholders.