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9 ML observability alternatives to Arize AI – 2024

alternatives to Arize AI
Dor Dor
5 min read Jul 23, 2023

Looking for ML observability alternatives to Arize AI? Check out these 9 solutions to help you get the most out of your ML models in production.

While Arize AI offers organizations a simple way to monitor ML models in production, it’s quite limited when it comes to providing actionable insights to improve model performance and investigate any issues in production.  

In the ever-evolving landscape of AI/ML, businesses are continually seeking ways to improve and scale their production ML. As a result, several alternative solutions have emerged to help organizations streamline and optimize their ML processes. In this blog post, we will focus on nine alternatives to Arize AI that are worth considering in 2024: Aporia, Deepchecks, TruEra, Robust Intelligence, Superwise, SageMaker, Fiddler AI, WhyLabs, and Seldon Core.

  1. Aporia

Aporia is the ML observability platform, trusted by data science teams and businesses in every industry to ensure high model performance, scale production ML, and increase revenue. With its diverse range of features and capabilities, Aporia ensures high model performance by detecting issues such as drift, degradation, bias, and data integrity problems at scale. 

Aporia supports all use cases and accommodates every model type, including LLMs, tabular models, NLP, and computer vision. 

By integrating Aporia in just minutes and connecting directly to your preferred data sources, Aporia eliminates the need for data duplication and data sampling, enabling real-time monitoring of billions of predictions without compromising data integrity. The platform’s customizable dashboards, monitors, and metrics allow users to make Aporia their own for tracking key metrics,  and identifying small segments with high business impact, which would be challenging to do manually. 

Furthermore, Aporia’s alerting system promptly notifies users of performance degradation or other issues, allowing them to take proactive measures to improve model performance and increase overall business impact. The platform offers a suite of features to manage and improve ML models in production:

  • ML Model Monitoring: Effectively detect drift, bias, performance degradation, and data integrity issues in your production models. 
  • Live Alerts: Get alerts directly to Slack, MS Teams, email, or other communication channels to simplify and expedite response.
  • Deep Model Visibility: See into your models’ performance and track their behavior. Customize dashboards to showcase both data science metrics and business KPIs. 
  • Centralized Model Management: Get a unified view of all your models under a single hub. 
  • Production IR: Respond to any incident in production with a collaborative, notebook-like environment. Be productive with your root cause analysis, gain actionable insights to improve model performance through:
    • Segment Analysis
    • Drift Analysis
    • Distribution Analysis
    • Data Stats
    • Embedding Projector for unstructured data
    • Reporting and documentation
  • Responsible AI: Aporia’s ML observability platform tackles the AI hallucination problem, enabling businesses to practice Responsible AI and ensuring that their AI products are transparent, fair, secure, and compliant with regulations. 
  1. Deepchecks

Deepchecks is an ML validation and monitoring platform that provides real-time insights into your models’ performance. The platform focuses on data validation, model validation, and monitoring, ensuring your ML models are accurate, reliable, and maintainable.

  1. TruEra

TruEra offers a model intelligence platform designed to optimize the quality, explainability, and fairness of machine learning models. The platform helps data scientists and ML engineers understand and troubleshoot their models to improve performance.

  1. Robust Intelligence

Robust Intelligence is an AI risk management platform that focuses on detecting and mitigating risks associated with AI deployments. It offers solutions to monitor, assess, and address issues like adversarial attacks, data drift, and model bias.

  1. Superwise

Superwise is an AI assurance platform that provides visibility, control, and governance for AI applications. It enables organizations to ensure their AI models are trustworthy, reliable, and compliant with regulatory requirements.

  1. SageMaker Model Monitor

Amazon SageMaker is a fully managed machine learning service that helps data scientists and developers build, train, and deploy ML models quickly and easily. SageMaker offers a wide range of tools and capabilities to support the entire ML lifecycle, from data preparation to monitoring and maintenance.

  1. Fiddler AI

Fiddler AI is an explainable AI platform that helps businesses gain insights into their AI models, ensuring transparency, trustworthiness, and compliance. With Fiddler AI, organizations can improve their AI systems’ performance while adhering to regulatory and ethical standards.

  1. WhyLabs

WhyLabs is a model observability platform that offers real-time monitoring and diagnostics for AI and ML applications. By providing a robust set of tools to detect data drift, identify anomalies, and troubleshoot issues, WhyLabs ensures that your ML models remain reliable and accurate over time.

  1. Seldon Core

Seldon Core offers an ML monitoring solution for tracking and managing models in production. It provides real-time performance monitoring, alerting for deviations, and supports features like drift detection. Users can monitor metrics such as accuracy, latency, and throughput.

Conclusion

Each of these alternative solutions to Arize AI offers a unique approach to help businesses improve and scale their production ML. By understanding your organization’s specific needs and priorities, you can choose the right platform to optimize your ML processes and drive better results in 2024.

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