Uncover the Power of Model Observability with Ray + Arize

Uncover the Power of Model Observability with Ray + Arize

Table of Contents:

1. Introduction
2. Background Experience
3. About Rise AI
4. ML Infra companies 4.1. Data preparation companies 4.2. Model building companies 4.3. Model deployment companies
5. Integrating Rise AI with Ray
6. Model Observability with Rise AI 6.1. Capturing model inputs and outputs 6.2. Troubleshooting, monitoring, and explaining models in real-time 6.3. Distribution checks 6.4. Feature analysis 6.5. Prediction quality analysis 6.6. Model performance tracking 6.7. Real-time alerts
7. Example of Ray and Rise AI integration
8. Troubleshooting a model in production
9. Data distribution changes in production
10. Challenges after model deployment 10.1. Model drift 10.2. Performance drift 10.3. Data drift 10.4. Concept drift 10.5. Black box models 10.6. Data quality issues 10.7. Model readiness
11. Benefits of using an observability platform like Rise AI 11.1. Automatic validation 11.2. Proactive issue detection
12. Conclusion

Introduction

In today's fast-paced world of machine learning (ML), there are numerous companies offering solutions to various aspects of ML infrastructure. However, it can be challenging to navigate the vast landscape of ML infrastructure and find the right tools for your needs. This article aims to provide a comprehensive overview of ML infrastructure companies, with a specific focus on Rise AI, an ML observability platform Based in Berkeley, California.

Background Experience

The author of this article, Aparna Lindakran, has a strong background in ML infrastructure. After graduating from Cal with a degree in Electrical Engineering and Computer Sciences (EECS), she worked on Uber's ML team for several years, where she contributed to building their ML platform, Michelangelo. Aparna then pursued a Ph.D. in Computer Vision at Cornell before dedicating her time fully to building Rise AI.

About Rise AI

Rise AI is a startup based in Berkeley, California, comprised of a team of experts who have previously worked on ML infrastructure and analytics platforms. The company aims to bring the best of both worlds together, offering a comprehensive ML observability platform. With Rise AI, users gain the ability to troubleshoot, monitor, and explain their models in real-time during production. The platform captures various signals from the models, allowing teams to analyze and understand their performance, make necessary improvements, and address issues promptly.

ML Infra Companies

The ML infrastructure space can be divided into three distinct groups: companies that aid in data preparation, those that facilitate model building, and those that support model deployment and ensure its success in a production environment. Each group plays a critical role in the ML workflow, and Rise AI aims to integrate seamlessly across the inference flow to provide holistic observability.

Integrating Rise AI with Ray

One noteworthy aspect of Rise AI is its integration with Ray, a popular ML framework. The platform enables users of Ray to serve models and achieve model observability simultaneously. This integration streamlines the process of deploying a model using Ray's endpoints, while also effortlessly logging the model inputs, outputs, and predictions to the Rise AI platform. By combining the strengths of Ray and Rise AI, users can leverage the power of both platforms to develop and monitor ML models effectively.

Model Observability with Rise AI

Central to Rise AI's value proposition is its ability to provide comprehensive observability for ML models. The platform captures and analyzes various metrics and signals to help teams gain insights into their models' behavior and identify any issues that may arise. This section will explore the key features and capabilities that Rise AI offers for model observability.

Example of Ray and Rise AI Integration

To demonstrate the power of Ray and Rise AI integration, Aparna provides a practical example. In this example, a model is deployed using Ray, and predictions are logged to the Rise AI platform using just a few lines of code. This streamlined workflow allows teams to quickly identify and address issues that may arise during the production stage.

Troubleshooting a Model in Production

A common challenge faced by ML practitioners is troubleshooting models in a production environment. This section walks through a step-by-step troubleshooting example, illustrating how Rise AI's observability platform can help identify and resolve issues. The example focuses on identifying the cause of false positive classifications in a loan initiation fraud detection model.

Data Distribution Changes in Production

Data distribution changes can significantly impact the performance of ML models in production. Rise AI provides tools to compare the distribution of data used during model training with the data encountered in production. By identifying variations in data distribution, teams can gain insights into potential performance issues and take corrective measures.

Challenges After Model Deployment

After deploying ML models, several challenges can arise that require careful Attention and monitoring. This section explores various challenges, including model drift, performance drift, data drift, concept drift, black box models, data quality issues, and model readiness. Each challenge is discussed, highlighting the potential impact on model performance and the importance of observability in addressing these issues.

Benefits of Using an Observability Platform like Rise AI

Using an observability platform like Rise AI offers several benefits for teams working with ML models. This section outlines two key benefits: automatic validation and proactive issue detection. With automatic validation, Rise AI accelerates the process of validating models before production deployment, saving time and ensuring readiness. Proactive issue detection empowers teams to detect and address issues before they escalate, improving the overall performance and reliability of ML models.

Conclusion

In conclusion, Rise AI offers a robust ML observability platform that addresses the challenges faced by teams working with ML models in production. By integrating seamlessly with frameworks like Ray, Rise AI provides users with a comprehensive solution for deploying, monitoring, and troubleshooting ML models. With its powerful features and capabilities, Rise AI empowers data scientists and ML engineers to focus on what they do best – building models – while ensuring the models perform optimally in real-world scenarios.

Highlights:

1. Rise AI is an ML observability platform that integrates seamlessly with frameworks like Ray.
2. The platform captures model inputs, outputs, and predictions in real-time for troubleshooting and analysis.
3. Rise AI helps users compare data distributions between training and production environments.
4. Challenges after model deployment, including drift and black box models, can be addressed using Rise AI's observability tools.
5. Rise AI provides automatic validation and proactive issue detection to enhance model performance and reliability.

FAQ:

Q: How does the integration between Rise AI and Ray work? A: Rise AI seamlessly integrates with Ray, allowing users to deploy models using Ray's endpoints while logging inputs, outputs, and predictions to the Rise AI platform.

Q: Can Rise AI handle data distribution changes in a production environment? A: Yes, Rise AI provides tools to compare data distributions between the training and production environments, helping teams identify variations that may impact model performance.

Q: What challenges does Rise AI's observability platform address after model deployment? A: Rise AI helps address challenges such as model drift, performance drift, data drift, concept drift, black box models, data quality issues, and ensuring model readiness.

Q: What are the benefits of using an observability platform like Rise AI? A: Rise AI offers automatic validation, accelerating the model validation process, and proactive issue detection, allowing teams to address issues before they escalate.