## Introduction to ML Monitoring with Evidently.ai

Whether you’re a fresher or an experienced professional in the Data industry, did you know that ML models can experience up to a 20% performance drop in their first year? Monitoring these models is crucial, yet it poses challenges such as data changes, concept alterations, and data quality issues. ML Monitoring aids in early detection of model performance dips, data quality issues, and drift problems as new data streams in. This prevents failures in the ML pipeline and alerts the team to resolve the issue. Evidently.ai, a powerful open-source tool, simplifies ML Monitoring by providing pre-built reports and test suites to track data quality, data drift, and model performance. In this beginner’s guide to ML Monitoring with Evidently.ai, you’ll learn effective methods to monitor ML models in production, including monitoring setup, metrics, integrating Evidently.ai into ML lifecycles and workflows, and more.

### Learning Objectives
– Apply statistical tests to detect data quality issues like missing values, outliers, and data drift.
– Track model performance over time by monitoring metrics like accuracy, precision, and recall using Evidently’s predefined reports and test suites.
– Create a monitoring dashboard with plots like target drift, accuracy trend, and data quality checks using Evidently’s UI and visualization library.
– Integrate Evidently at different stages of the ML pipeline – data preprocessing, model evaluation, and production monitoring – to track metrics.
– Log model evaluation and drift metrics to tools like MLflow and Prefect for a complete view of model health.
– Build custom test suites tailored to your specific data and use case by modifying its parameters.

*This article was published as a part of the Data Science Blogathon.*

In the ever-changing landscape of the data industry, it’s crucial to monitor machine learning models to ensure they maintain their performance over time. Studies have shown that ML models can experience up to a 20% performance drop in their first year, highlighting the need for continuous monitoring.

ML monitoring helps in early detection of model performance dips, data quality issues, and drift problems as new data streams in. This proactive approach prevents failures in the ML pipeline and alerts the team to resolve the issue promptly. Evidently.ai, an open-source tool, simplifies ML Monitoring by providing pre-built reports and test suites to track data quality, data drift, and model performance.

In this beginner’s guide to ML monitoring with Evidently.ai, you will learn effective methods to monitor ML models in production. Some learning objectives include applying statistical tests to detect data quality issues like missing values, outliers, and data drift, tracking model performance metrics over time, and integrating Evidently.ai into ML lifecycles and workflows.

Understanding ML Monitoring and Observability in AI Systems

ML Monitoring and Observability are essential components of maintaining the health and performance of AI systems. ML Monitoring tracks the behavior of models at various stages, including production, candidate model comparison (A/B tests), and live models in the real-world application. It’s not just about monitoring the model; it also involves monitoring the overall health of the software system.

ML Observability, on the other hand, goes beyond monitoring and focuses on understanding the overall system behavior, identifying root causes, and making informed decisions. It helps in finding the actual underlying issues that impact model performance and data quality.

Key Considerations for ML Monitoring

– Create an ML monitoring setup tailored to specific use cases.
– Choose model re-training strategies based on the use case.
– Use reference datasets for comparison and drift detection.
– Build custom user-defined metrics for monitoring specific KPIs.

ML Monitoring Architecture

ML monitoring involves collecting data and performance metrics at different stages, including backend monitoring, alerts, reports, and dashboards. Backend monitoring includes data pipelines, batch monitoring, and real-time monitoring to track model predictions, data quality, and drift.

ML Monitoring Metrics: Model Quality, Data Quality, Data Drift

To evaluate ML model quality, it’s essential to use a combination of standard metrics like precision and recall along with custom metrics tailored to the business’s specific needs. Data quality evaluation involves checking for data anomalies, missing values, and outliers using statistical tests, distance-based tests, and rule-based tests. Data drift detection methods include statistical tests, distance-based tests, and custom metrics to monitor changes in data distribution over time.

Integrate Evidently in a Prefect Pipeline

By integrating Evidently into a Prefect pipeline, you can automate data drift and model quality checks at different stages. The pipeline includes tasks to load data, preprocess data, run data drift tests, and define the flow. Prefect allows for orchestrating ML workflows and automating monitoring processes.

Integrate Evidently with MLflow

Integrating Evidently with MLflow allows for logging data drift test results and metrics to MLflow for centralized tracking and management. By defining tasks to load data, preprocess data, and log metrics to MLflow, you can streamline the monitoring process and ensure data consistency across the pipeline.

ML Monitoring Dashboard

Dashboards play a crucial role in visualizing and monitoring metrics over time. Panels and metrics like data profile, target drift, data quality checks, accuracy plots, prediction drift data help in analyzing dataset issues, model performance, and feature importance. By creating a batch monitoring dashboard, you can track key metrics and take necessary measures to maintain model accuracy.

Conclusion

In conclusion, effective ML monitoring and observability are essential for maintaining model performance and data quality in the ever-evolving data industry. By leveraging tools like Evidently.ai and integrating them into ML workflows, teams can proactively detect issues, make informed decisions, and ensure the continuous improvement of their AI systems. This guide provides valuable insights and practical methods for implementing ML monitoring strategies in real-world applications.

This article is a comprehensive guide to ML monitoring and observability in AI systems, covering key concepts, methodologies, best practices, and tools for successful implementation. Whether you’re a beginner or an experienced professional in the data industry, understanding ML monitoring is crucial for ensuring the success of machine learning projects.