Boost Your Product with Arize

Boost Your Product with Arize

Table of Contents

1. Introduction
2. Overview of Arise ML Observability Platform
3. Login and Quick Overview
4. Model Schema and Tracking Performance Metrics
5. Monitoring Features and Outputs
6. Understanding Model Behavior
   • 6.1 Drift View
   • 6.2 Feature Drift View
   • 6.3 Temporal View
7. Taking Action: Retraining Models
8. Creating Custom Dashboards
9. Applying Filters and Analyzing Specifics
10. Exporting Data for Training Pipeline
11. Troubleshooting Workflow
12. Stakeholder Views and Actionable Items
13. Comparing New and Old Models
14. Conclusion

Introduction

In the world of machine learning, observability plays a crucial role in ensuring the performance and reliability of models. Arise is an ML observability platform that aims to help ML teams automatically surface, understand, and resolve issues that may occur in their models. By providing a comprehensive overview of all models in production, Arise assists in improving the overall model performance. This article will explore the various features and functionalities offered by Arise and how it can benefit ML teams in their model monitoring and troubleshooting processes.

Overview of Arise ML Observability Platform

Arise provides ML teams with a powerful platform to gain insights and take actions to enhance the performance of their machine learning models. By automatically discovering a model's schema and tracking key performance metrics, Arise allows teams to identify any health issues quickly. The platform supports production, validation, and training data sets, enabling the establishment of baselines for comparison. Not only does Arise monitor metrics, but it also focuses on understanding the root causes of model behavior.

Login and Quick Overview

Upon logging into the Arise platform, users are greeted with a high-level overview of all models in production. This overview page serves as a single interface to identify any health issues associated with the models. Additionally, performance drift and data quality issues are highlighted, giving users a quick and comprehensive view of their model's Current state.

Model Schema and Tracking Performance Metrics

Arise automatically detects a model's schema and starts tracking key performance metrics. These metrics include cardinality, percent empty, drift, and quantiles. The platform supports production, validation, and training data sets, allowing users to configure baselines or benchmarks for comparison. Tracking these metrics provides essential insights into the model's performance and helps identify any discrepancies or anomalies.

Monitoring Features and Outputs

Monitoring is a critical aspect of model observability. Arise monitors all features and outputs of a model, creating default thresholds for anomaly detection. These thresholds can be customized Based on the specific needs of the business. By constantly monitoring features and outputs, ML teams can gain real-time insights into the behavior of their models, detecting any deviations from expected distributions.

Understanding Model Behavior

To fully understand why a model is behaving in a certain way, Arise provides several views that Delve deeper into model behavior.

6.1 Drift View

The drift view allows users to assess the extent to which model outputs deviate from the distribution of the baseline. By comparing the predicted values to the expected distribution, ML teams can quickly identify instances where the model is generating unexpected results. This view enables users to pinpoint areas of concern and take necessary actions to address any drift effectively.

6.2 Feature Drift View

The feature drift view helps ML teams identify the most significant features driving model drift. It provides insights into which features within the model have the most influence on its behavior. By understanding these crucial features, teams can focus their efforts on evaluating and improving the corresponding areas to minimize drift and enhance overall model performance.

6.3 Temporal View

The temporal view allows users to analyze the performance of specific features over time. By comparing feature distributions in relation to the baseline, ML teams can observe any discrepancies and Patterns that may arise. This view helps identify time-based trends and provides insights into when and how certain features deviate from the expected distribution. Armed with this information, teams can make informed decisions on updating their model training data sets.

Taking Action: Retraining Models

When observations from Arise indicate the need for model retraining, the platform facilitates the process seamlessly. ML teams can leverage Arise to Create dashboards with customizable configurations. These dashboards enable users to analyze and understand their models in ways that Align with their business needs. By selecting Relevant features and specifying the positive class (e.g., fraud), users can gain valuable insights into overall model volume, distribution, and performance metrics.

Creating Custom Dashboards

Arise empowers ML teams to create free-form dashboards that cater to their unique requirements. These dashboards can be customized to showcase the metrics, features, and information that matter most to the team. By designing personalized dashboards, users can gain a holistic view of their model's performance and identify areas that require Attention.

Applying Filters and Analyzing Specifics

Arise allows users to Apply filters to the aggregated data, enabling focused analysis on specific segments. By filtering data based on attributes, such as location or time period, ML teams can gain granular insights into model performance. These filters aid in identifying specific regions or time intervals where the model may exhibit varying behaviors. By understanding these specifics, teams can streamline their efforts in optimizing the model for these specific segments.

Exporting Data for Training Pipeline

When ML teams need to use specific data sets for model retraining, Arise simplifies the process by allowing seamless data exports. Users can export the required data, which becomes readily available for integration into the model's training pipeline. This streamlined workflow ensures that teams can make data-driven decisions to address observed issues and improve overall model performance.

Troubleshooting Workflow

Arise serves as a comprehensive tool for ML troubleshooting. By providing different views and analysis options, the platform guides users through the entire troubleshooting workflow. From identifying anomalies and understanding root causes to taking corrective actions, Arise offers a seamless experience that enables ML teams to address issues efficiently.

Stakeholder Views and Actionable Items

Arise caters to various stakeholders by providing different views tailored to their needs. Whether it's team members responsible for model performance, data scientists, or business analysts, each stakeholder can access the platform to derive actionable insights. Arise identifies issues and offers recommendations for retraining and improving models, ensuring that the right stakeholders have access to the right information to drive positive outcomes.

Comparing New and Old Models

When ML teams are ready to compare the performance of a new model against the existing one, Arise provides templates for seamless comparison. Users can leverage these templates to analyze the differences in model performance and identify areas of improvement. By comparing new and old models, teams can assess the impact of changes and make informed decisions on which version to deploy.

Conclusion

In summary, Arise is a powerful ML observability platform that empowers ML teams to monitor, understand, and improve the performance of their models. By providing extensive insights into model behavior, facilitating troubleshooting workflows, and offering actionable recommendations, Arise is an invaluable tool for ensuring the reliability and optimization of machine learning models.

Highlights:

• Arise is an ML observability platform that helps ML teams improve their model's performance.
• The platform offers a quick high-level overview of all models in production, highlighting performance drift and data quality issues.
• Arise automatically discovers a model's schema and tracks key performance metrics.
• Monitoring features and outputs, as well as understanding model behavior, are crucial aspects facilitated by Arise.
• The platform provides various views, such as Drift View, Feature Drift View, and Temporal View, to analyze and address model behaviors and inconsistencies.
• By leveraging Arise, ML teams can create custom dashboards, apply filters, and export data for their training pipeline.
• Arise assists in streamlining the troubleshooting workflow, ensuring different stakeholders have access to actionable items for model improvement.
• The platform also enables easy comparison between new and old models, aiding decision-making on model deployment.

FAQs:

Q: Can Arise be used to monitor multiple models simultaneously? A: Yes, Arise provides a comprehensive overview of all models in production and supports monitoring for multiple models simultaneously.

Q: How does Arise detect and track model drift? A: Arise compares the distribution of model outputs against the baseline distribution to identify and track model drift over time.

Q: Can I customize the dashboards in Arise to focus on specific metrics and features? A: Yes, Arise allows users to create custom dashboards that cater to their specific needs, enabling focused analysis on selected metrics and features.

Q: How does Arise help in troubleshooting model issues? A: Arise offers various views and analysis options to understand the root causes of model behavior. It provides actionable items for retraining and improving models, guiding teams through an efficient troubleshooting workflow.

Q: Can I export data from Arise for model retraining? A: Yes, Arise allows seamless data exports, making the data readily available for integration into the model's training pipeline.

Q: Can Arise compare the performance of a new model against an existing one? A: Yes, Arise provides templates for comparing new and old models, enabling users to assess the differences in performance and make informed decisions on deployment.