ML Observability Tools: Arize AI Alternatives

Machine learning (ML) observability tools are indispensable for data scientists and developers. They provide deep insights into understanding, monitoring, and improving the performance of ML models. 

These tools are crucial in identifying potential issues, such as prediction drift or data anomalies, and facilitating performance improvements. Beyond monitoring real-time performance and ensuring model accuracy, observability tools are pivotal in promoting accountability, especially in navigating the complex ethical issues in AI.

Arize AI is an ML observability platform that provides real-time model performance monitoring, anomaly detection, and root cause analysis. It integrates with existing ML pipelines with metrics and visualizations to track model reliability over time. This is critical for managing and improving the functionality of ML models, even large language models (LLMs), which present unique challenges due to their complexity and scale.

But what if Arize AI is not the right fit for you or what your project needs? Finding suitable alternatives is a good next step. This article guides you through 12 alternative tools for Arize AI. Each tool addresses specific challenges and may be worth exploring based on our requirements. Here’s the list: 

1. Encord Active
2. Aporia
3. DeepChecks
4. Fiddler AI
5. Superwise
6. SageMaker Model Monitor
7. WhyLabs 
8. IBM Watson Studio
9. Voxel51
10. Azure Machine Learning
11. Databricks and 
12. Vertex AI. 

Let us take a closer look at these alternatives.

Encord Active

Encord is an end-to-end computer vision (CV) platform that enables you to annotate and curate datasets through AI-assisted features. The platform’s data-centric approach helps you curate high-quality data, evaluate and monitor models, and use active learning techniques to improve them.

Optimize model performance with Encord Annotate and Active.

Encord Active is part  of the suite of Encord tools that help you monitor models based on the quality of data and predictions:

• Encord Annotate: Includes basic and advanced features for labeling image data for multiple CV use cases.
• Encord Active: Monitor models based on data quality and use the AI-assisted features to perform root-cause analysis.
• Index: Helps curate and manage multi-modal data.

Encord Active.

Key Features

• Scalability: The platform allows you to upload up to 500,000 images (recommended), 100 GB in size, and 5 million labels per project. You can also upload up to 200,000 frames per video (2 hours at 30 frames per second) for each project. See more guidelines for scalability in the documentation.
• Data Quality Monitoring: Encord Active has the advantage of native integration with a smart annotation tool in Annotate. For a project, it monitors the quality of input data, checking for missing values, outliers, and ensuring data types and schema are consistent with what the model expects using data quality metrics.
• Customization: It provides the option of monitoring custom metrics you use to evaluate your model’s performance and data quality outside the extensive pre-built metrics. You can upload your predictions manually through the UI or programmatically from your production environment through the SDK.
• Auto-Discovery: Encord Active provides a quick method to filter unwanted or problematic images by auto-discovering the issues (e.g., anomalies, label quality, etc.) in your model predictions with ML.
• Feature Importance Tracking: Track the impact of label and data quality on the features your model predicts to get granular insights into your model’s performance scores for better debugging. 
• Model Version Tracking: Active allows you to track multiple prediction sets from different model versions and compare the performance of models based on quality metrics on your validation or production data.
• Compliance and Governance: Encord complies with the General Data Protection Regulation (GDPR), System and Organization Controls 2 (SOC 2), and Health Insurance Portability and Accountability Act (HIPAA) standards. It uses advanced encryption protocols to ensure data security and privacy.
• Collaboration: It allows users a way to communicate with annotators, reviewers, team managers, and administrators within annotation projects and active projects through comments.
• Model Optimization Workflow: Because Active also provides insights into the data quality and is tightly integrated with an annotation tool, you can build a loop of continually improving your model by curating high quality data, evaluating model performance, adding newly annotated images, re-training, and performing comparisons against production models.

Best for

• Computer Vision Engineers who prioritize data quality, comprehensive model evaluation tools, and a data-centric approach to improve production model performance.

Pricing

• Encord has a pay-per-user pricing model with Starter, Team, and Enterprise options.

Evaluate your models and build active learning pipelines with Encord

Try Encord today

Aporia

Aporia is an ML observability platform for data science teams across various industries. It supports all use cases and accommodates various model types, including LLMs, tabular models, NLP, and computer vision. 

Aporia Dashboard

Key Features 

• Scalability: Aporia focuses on real-time monitoring of billions of predictions without compromising data integrity, suggesting it is good at scaling to large datasets. 
• Customization: Aporia offers customizable dashboards, thresholds, and language flexibility.
• Better Insights: Aporia provides comprehensive observation capabilities, addressing ethical concerns and enhancing interpretability. 
• Centralized Model Management: Aporia's centralized system provides comprehensive observability across models, focusing on drift, bias, and feature importance.
• Production Incident Response: Aporia is a collaborative notebook-like environment for root cause analysis and actionable insights, making it a solid choice for production incident response.

Best for

• CV engineers, ML engineers, data scientists, and researchers. 

Pricing

• Free tier for its Guardrails (protecting from LLM risks) and Observe (monitoring ML models) products. Paid plans offer more features, users, tokens (for Guardrails), and functionalities like custom dashboards (Observe) with custom pricing for Enterprise plans.

DeepChecks

DeepChecks is a holistic open-source solution for all your AI and ML validation needs. It enables comprehensive testing of data and models from research to production. It ensures model quality, compliance, and robustness, making it an essential toolkit for NLP practitioners, researchers, and organizations.

Deepchecks

Key Features

• Scalability: It's built to handle a large number of models in parallel and works well even as the data scales.
• Customization: It emphasizes flexibility, customizable dashboards, and proactive alerts to track data science metrics and business KPIs.
• Beta Program for GPT-4: This is not just a program; it’s a ticket to explore next-gen LLMs like GPT-4.

Best for 

• NLP practitioners, researchers, and organizations who want comprehensive oversight of their language models.  

Pricing

• Different pricing tiers, including Startup, Scale, and Dedicated plans, cater to various needs and ensure user flexibility.

Fiddler AI

Fiddler AI is a pioneering platform for AI Observability that empowers enterprises to monitor, explain, analyze, and improve their models and generative AI applications. Fiddler ensures high performance, model quality, and responsible AI practices by providing actionable insights and transparency.

It offers interactive, explainable AI, model monitoring, and support for the entire ML lifecycle.

Fiddler AI

Key Features 

• Scalability: It unifies model monitoring, explainable AI, performance analytics, and fairness capabilities within a single system. That is why, in terms of scalability, Fiddler AI stands out for its purpose-built platform, unified features, enterprise-scale capabilities, and actionable insights. 
• Customization: Fiddler AI is a customizable monitoring tool with dashboards, thresholds, and metrics for enhanced responsiveness and agility. It allows users to track key metrics and meet business requirements.
• Better Insights: Fiddler AI covers explainability techniques like feature attribution, SHAP values, and counterfactuals and allows users to customize explanation details.
• Diverse Features: Fiddler AI stands out from Arize AI due to its customizable analytics and what-if analysis capabilities. It also prioritizes regulation-friendly transparency and compliance, ensuring responsible AI practices while adhering to privacy standards.

Best for 

• Data scientists, machine learning engineers, and AI practitioners who need comprehensive model monitoring, explainability, and compliance features.

Pricing

• Pay-per-use pricing model for users to pay for the specific features and resources they utilize. This flexible approach ensures cost-effectiveness and scalability based on individual needs and usage patterns.

Superwise

Superwise AI is an observability platform designed to enhance the management and monitoring of models in production. It provides visibility and context into model behavior, allowing teams to identify anomalies, assess risks, and collaborate effectively to resolve issues.

Superwise AI

Key Features 

• Scalability: Superwise AI suits businesses with numerous models, handling large-scale ML operations.
• Customization: Superwise provides customizable monitoring features to meet users' business needs.
• Better Insights: SuperWise AI ensures the trustworthiness of AI models with customizable monitoring features.
• Diverse Features: SuperWise AI provides observability, customization, and insights for ML models. It includes AI performance management, bias detection, explainability, and analytics capabilities.

Best for 

• Data scientists and ML engineers seeking trustworthy, reliable, and compliant AI models.

Pricing

• Flexible pay-as-you-go pricing model without feature lockouts, where costs are based on model usage. The first three models are free with up to 1 million monthly predictions, and volume discounts are available as usage grows.

SageMaker Model Monitor

Amazon SageMaker is a fully managed ML service provided by AWS. It simplifies the process of building, training, and deploying ML models at scale. With SageMaker, data scientists and developers can quickly and confidently create, train, and deploy ML models in a production-ready hosted environment.

Amazon Sagemaker

Key Features 

• Scalability: Amazon SageMaker offers robust scalability, a comprehensive integrated development environment (IDE), and seamless integration within the AWS ecosystem.
• Customization: Amazon SageMaker offers extensive customization options, allowing users to tailor ML workflows, choose from various instance types, and leverage AWS services. SageMaker supports a wide range of frameworks and algorithms, providing high customization.
• Better Insights: Amazon SageMaker provides tools for data scientists and developers to gain better insights from their data. Features like SageMaker Canvas chat for data prep and Foundation model evaluations (FMEval) help users create data preparation flows and quantify the risk of providing inaccurate or biased content with their language models.
• Diverse Features: Amazon SageMaker offers features catering to various ML aspects. This includes the Code Editor for writing and testing code, Deep learning containers for large model inference, and SageMaker HyperPod for developing large models.

Best for 

• Data and ML teams that are already building and deploying their tech stack on AWS.

Pricing

• Pay-as-you-go pricing model where you are billed by the second for building, training, and deploying models, with no minimum fees or upfront commitments. 

WhyLabs

WhyLabs is an AI observability platform that prevents data quality issues and model performance degradation. By monitoring data pipelines and machine learning models in production, WhyLabs helps detect data drift, concept drift, and data quality issues. The platform is based on the Whylogs open-source library, which generates statistical summaries for privacy-preserving observability and monitoring at scale.

WhyLabs AI

Key Features 

• Scalability: WhyLabs offers a platform that scales seamlessly with your data. It can handle massive amounts of data and provide real-time actionable insights, making it suitable for large-scale AI operations.
• Customization: WhyLabs seamlessly integrates with existing data pipelines, allowing tailored customization for specific workflows. 
• Better Insights: With WhyLabs, teams can detect data and model drift, identify training-serving skew, and continuously monitor key performance metrics.
• Diverse Features: The platform supports structured or unstructured data, batch or streaming data, and integrates with various data platforms. It offers functionalities like outlier detection, histograms, data drift monitoring, and missing feature values, catering to a wide range of AI monitoring needs.

Best for 

• Organizations (including financial services, logistics, manufacturing, retail, e-commerce, and healthcare), data engineers, and machine learning practitioners that need to monitor and maintain the quality of their data pipelines and ensure that models perform optimally in production.

Pricing

• Structured to accommodate a range of users, offering a complimentary Starter plan for individuals, an Expert plan for smaller teams, and customized solutions for larger enterprises with specific scale, security, and support needs.

IBM Watson Studio

IBM Watson Studio is an integrated development environment (IDE) that empowers data scientists, developers, and analysts to build, run, and manage AI models. It is available as Software as a Service (SaaS) or for self-hosting as part of IBM Cloud Pak for Data. With Watson Studio, you can work with open-source frameworks, visual modeling, MLOps, decision optimization, NLP, and generative AI. It provides a collaborative workspace for solving business problems through data science.

IBM Watson Studio

Key Features 

• Scalability: Watson Studio supports an open multi-cloud architecture, which allows for scaling AI models across different cloud environments. This ensures that as your data grows or your computational needs change, providing the flexibility to build, run, and manage AI models at scale. 
• Customization: The platform offers a range of tools and languages such as Python, R, and Scala, and integrates with open-source frameworks like PyTorch, TensorFlow, and scikit-learn..
• Better Insights: Watson Studio provides capabilities for visual modeling, decision optimization, and natural language processing, which can help in gaining better insights from your data. 
• Diverse Features: Watson Studio combines features such as MLOps, decision optimization, visual modeling, and AI governance. It also offers tools for data preparation, model building, and deployment, making it a comprehensive solution for various data science and ML tasks.

Best for 

• Data scientists, developers, and analysts who need an integrated environment for building, running, and managing AI models.

Pricing

• Flexible pricing model that includes both a free Lite plan and a professional plan. The Professional plan operates pay-as-you-go, charging for compute usage measured in capacity unit hours.

Voxel51

Voxel51 is an AI software company that provides open-source computer vision tools for ML. Their flagship product, FiftyOne, enables data scientists to curate and experiment with datasets, leading to the development of higher-performing machine learning models. With applications in various sectors such as agriculture, aviation, healthcare, retail, and manufacturing, Voxel51 aims to enhance the quality and efficiency of computer vision workflows.

Voxel51

Key Features 

• Scalability: FiftyOne can manage billions of samples and supports secure collaboration on datasets in the cloud, making it highly scalable for large-scale projects.
• Customization: While Voxel51 provides intuitive visualizations and allows users to add metadata to samples, its customization options are limited.
• Better Insights: Voxel51’s FiftyOne Teams enable better insights by identifying data problems early, validating hypotheses, and improving solution engineering for products and services.
• Diverse Features: It offers a range of features, including the ability to clean and relabel datasets efficiently, convert model predictions into training datasets, and organize large research datasets. Users can view distributions over image attributes and filter datasets by those attributes.

Best for

• Data scientists, machine learning engineers, and computer vision researchers who work with large-scale datasets and need powerful tools for data exploration, visualization, and curation.

Pricing

• Voxel51 offers its core computer vision toolkit, FiftyOne, as an open-source platform, which means it is available free of charge. They also provide a free trial for their Teams version, which includes additional features for collaboration and dataset management in the cloud.

Azure Machine Learning

Azure Machine Learning (Azure ML) is a cloud-based platform that empowers data scientists, developers, and businesses to create, train, and deploy machine learning models. With Azure ML, you can build pipelines, experiment with algorithms, and integrate predictive models into your applications.

Azure Machine Learning

Key Features 

• Scalability: Azure ML provides a highly scalable platform that allows for the dynamic adjustment of resources to meet demand. It supports horizontal and vertical scaling, ensuring your applications can handle varying loads efficiently. 
• Customization: The platform balances out-of-the-box solutions and custom-built options, allowing for precise tailoring of services to meet specific needs. This means you can select services that are both forward-thinking and customized, considering the trade-offs between convenience and customization.
• Better Insights: Azure ML provides advanced analytics and automation capabilities. This empowers businesses to leverage better insights from their data, enabling informed decision-making and strategic planning.
• Diverse Features: Azure’s diverse features include secure storage options, reliability, and diverse data handling capabilities. The platform’s extensive suite of services covers various aspects of cloud computing, from databases and AI to DevOps and the Internet of Things (IoT), ensuring a comprehensive set of tools.

Best for 

• Data scientists, developers, and enterprises building on Microsoft Azure and seeking a comprehensive platform for building, training, and deploying models.

Pricing

• Flexible pricing options, including pay-as-you-go, savings plans, and reserved instances. Additionally, Azure offers options for reserved instances and spot instances for more cost-effective solutions. 

Databricks

Databricks is a platform that uses generative AI within the context of a data lakehouse. It understands the unique semantics of your data, optimizes performance, and manages infrastructure to match your business needs.

Databricks AI

Key Features 

• Scalability: Databricks provides a scalable and reliable platform capable of handling large datasets and complex workflows. This allows for dynamic scaling of compute resources based on workload demands.
• Customization: Users can build and manage complex data pipelines using various programming languages, including Python, Scala, and R. Databricks provides comprehensive customization for data processing, analytics, and machine learning workflows.
• Better Insights: Databricks allows users to share notebooks, data, and insights with team members, facilitating real-time collaboration on data engineering and machine learning tasks, and deriving better insights.
• Diverse Features: Databricks integrates with various Azure services, providing a comprehensive environment for data engineering, data science, and analytics. It also automates cluster provisioning, auto-scaling, and job scheduling, contributing to a diverse set of features that enhance the overall functionality of the platform.

Best for 

• Data and ML teams that want to work very closely.

Pricing

• Consumption-based pricing model, where users are billed for the resources they utilize, such as virtual machines, runtime hours, and data storage. The platform offers flexible pricing tiers and committed-use discounts for different usage levels, allowing for cost savings compared to pay-as-you-go rates.
  

Vertex AI

Vertex AI is a Google Cloud ML platform that provides a unified environment for training, deploying models, and customizing large language models (LLMs) for AI applications. The platform offers options for AutoML, custom training, and access to generative AI models for various modalities like text, code, images, and speech.

Vertex AI Dashboard

Key Features 

• Scalability: Vertex AI provides scalability through distributed training across GPUs and TPUs, allowing for the training of models at scale. Vertex AI focuses on scalability through distributed training and serverless model serving. 
• Customization: The platform includes multiple customization options, supports various ML frameworks like TensorFlow, GPTConsole, Slingshot, Cameralyze, and Kedro, allowing for a tailored ML experience. Vertex AI provides customized ML models through AutoML and custom training.
• Better Insights: Vertex AI provides better insights by making the ML journey more approachable for data scientists and integrating with popular ML frameworks.
• Diverse Features: It includes over 100 foundation models. Users work with Vertex AI to automate or streamline their ML projects with functionalities like data preparation, model training, model evaluation, and more.

Best for 

• Computer vision, ML engineers, and data scientists who build, deploy, and manage models on Google Cloud.

Pricing

• Flexible pricing model based on usage. For custom models, you pay for training based on data size and complexity, and for deployment, you’re billed per model endpoint. Generative AI charges depend on input and output characters. 

Arize AI Alternatives: Key Takeaways

ML Observability Tools are essential for data scientists and developers to understand, monitor, and improve the performance of machine learning models. Arize AI is a platform that features AI observability, automated model monitoring, embedding and cluster evaluation, and prompt iteration/troubleshooting.

• Benefits of Arize AI: Arize AI is a machine learning observability platform that offers real-time monitoring, anomaly detection, and root cause analysis. It integrates with existing ML pipelines, providing metrics and visualizations to track model accuracy and dependability over time, including AI and ML observability.
• Limitations of Arize AI: Like any other tool, Arize AI faces challenges like model drift, performance degradation, data drift, concept drift, black box models, data quality issues, and model readiness. Alternative tools may offer better monitoring, interpretability, data quality, and readiness checks.
• Alternatives to Arize AI (2024): Notable alternatives include Encord Active, Aporia, DeepChecks, Fiddler AI, Superwise, SageMaker Model Monitor, WhyLabs, IBM Watson Studio, Voxel51, Azure Machine Learning, Databricks, and Vertex AI, that offer larger scalability, more customization freedom, better insights, and other diverse features.

Evaluate your models and build active learning pipelines with Encord

Try Encord today