Top 9 Alternatives to DeepChecks

Machine learning (ML) and artificial intelligence (AI) are the top buzzwords redefining technology in the 21st century. Almost half of all businesses - big and small - use ML globally to achieve their goals and drive productivity.

Unfortunately, most ML initiatives do not see the light of day due to bottlenecks across the development cycle that prevent businesses from successfully deploying an ML application. Around 43% of data scientists say that 80% of models never go into production.

One solution lies in using platforms and tooling that can rigorously test and evaluate ML models for performance, robustness, compliance, bias, computational efficiency, and business metrics.

DeepChecks is a popular choice due to its strong testing and validation functionalities. However, the tool only focuses on model validation and does not address advanced ML model evaluation or data monitoring challenges.

In this article, we provide an overview of the nine best alternatives listed below that will help you select a suitable tool based on your specific needs.

• Encord Active
• Lightly
• TELUS International
• Aquarium
• Voxel51
• Arize
• Hive
• Arthur
• Amazon SageMaker

DeepChecks

DeepChecks is an evaluation platform for validating LLMs and ML models. It offers three solutions: LLM Evaluation, ML Monitoring, and Open Source Testing.

The LLM Monitoring solution features multiple metrics for LLM validation, helping you assess response quality, manage training datasets, and debug issues.

Monitoring is a continuous validation framework that monitors ML models and data throughout the machine learning lifecycle. The solution also features basic MLOps tools for continuous integration (CI) and deployment (CD) to help ML engineers evaluate production models.

Finally, Open Source Testing is a Python package that helps you validate ML models and datasets end-to-end. The open-source tool comprises several suites to assess training and test sets, model performance, and data integrity.

Challenges

Although DeepChecks helps you validate LLM apps and ML models, it has some challenges that make it unsuitable for managing more complex deep learning frameworks, such as visual foundation models (VFMs), multi-modal models, and object-tracking architectures.

• Limited Computer Vision Functionality: Deepchecks does not provide features to annotate and curate computer vision (CV) datasets. This means users need to find other solutions to create and manage training datasets.
• Lack of Collaboration Tools: The solution lacks collaborative tools for managing projects across teams, preventing users from providing feedback, sharing projects, and sharing resources.
• Requires Coding Expertise: While LLM and ML Monitoring provide a no-code interface for users, the Open Source Tool requires coding expertise in Python to validate models.
• No Security Compliance: The solution doesn't have any official compliance certifications, such as those from the General Data Protection Regulation (GDPR), the International Organization for Standardization (ISO), and System and Organization Controls SOC).

So, let’s discuss a few viable alternatives to DeepChecks in the next section.

DeepChecks Alternatives

With the artificial intelligence (AI) landscape continuously evolving with sophisticated modeling frameworks and various data types, users require robust solutions to build scalable ML applications.

The tool must have the following features to streamline the development process:

• Support for managing multiple data types to help curate data for multi-modal models.
• Collaboration tools to help members of large teams provide and address valuable expert feedback.
• Easy-to-use interface to help new users utilize the platform to its full potential.
• Data security to ensure data privacy and compliance with international standards.
• Other advanced features for managing complex unstructured datasets, like medical and sensor data.

The list below provides an overview of the top alternatives, ranked according to these factors.

Encord

Encord is an end-to-end data-centric AI platform for annotating, curating, and monitoring large-scale datasets. It offers the following tools with native integration for streamlined performance:

• Encord Annotate: Includes basic and advanced features for labeling image data for multiple CV use cases.
• Encord Active: Supports active learning pipelines for debugging datasets.
• Index: Helps curate multi-modal data for effective management.

Encord

Key Features

• Supported Data Types: Encord supports image, video, Digital Information and Communications in Medicine (DICOM), Neuroimage Informatics Technology Initiative (NIfTI), and Electrocardiogram (ECG) data.
• Collaboration: The platform allows you to create organizations and assign relevant roles to team members for effective cross-communication. Users also benefit from workflow templates to manage projects at different stages of the development lifecycle.
• Intuitive UI: Encord offers an easy-to-use, no-code UI with self-explanatory menu options and powerful search functionality for quick data discovery. Users can provide queries in everyday language to search for images and use relevant filters for efficient data retrieval. You can also use the SDK to access your projects programmatically.
• Data Security: The platform is compliant with major regulatory frameworks, such as the General Data Protection Regulation (GDPR), System and Organization Controls 2 (SOC 2 Type 1), AICPA SOC, and Health Insurance Portability and Accountability Act (HIPAA) standards. It also uses advanced encryption protocols to protect data privacy.

Other Features

• Embedding Plots: Encord Active lets you interactively visualize high-dimensional data in two-dimensional grids. You can surgically select specific data points based on the required criteria.
• Automation: Encord Annotate features multiple automated labeling frameworks, including segment anything model (SAM), interpolation, and object tracking algorithms to speed up the annotation process.
• Data Storage: Index provides flexible storage functionality to organize your datasets. It features synced folders, allowing you to update all your datasets linked to this folder quickly.

Best For

• Teams that wish for a scalable data-centric solution with features to streamline computer vision data and model quality evaluation with native integration with annotation and data management tools.

Pricing

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

Lightly

Lightly is a data curation platform for CV projects. It uses active learning data pipelines to help you select the most relevant data samples for model training.

It also uses embeddings, metadata, and model predictions to choose appropriate data points.

Lightly

Key Features

• Supported Data Types: Lightly supports images, sequences, and videos.
• Collaboration: Users can coordinate with their team members through service accounts and assign them relevant roles according to their expertise.
• User Interface: The Lighlty Platform offers an intuitive UI to view and analyze the selected datasets.

Other Features

• Custom Embeddings: Lightly lets you customize image embeddings to suit unique image types and view them.
• Corruption Checks: The platform features built-in frameworks to identify broken files by assessing whether users can quickly access, open, and decode image files.

Best For

• Teams looking for an easy-to-use model validation solution with basic functionalities.

Pricing

• Lightly offers a community, team, and custom version.

Telus International

Telus International offers an AI-based solution for managing training data through Ground Truth Studios (GT). It has three variants: GT Manage, GT Annotate, and GT Data.

Telus International GT Studio

Key Features

• Supported data types: GT Annotate supports video, text, 3D sensor fusion, audio, and image data.
• Collaboration: GT Manage provides tools for workforce management and lets you scale contributors as per requirements.
• Data Security: The platform is SOC 2-compliant and features encryption and firewall applications for added security.

Other Features

• Performance Monitoring: The platform offers real-time monitoring to track annotation speed, project timelines, and labeling quality.
• Flexible Tools: The solution offers APIs for creating customized annotation pipelines.

Best For

• Global teams looking for a data management solution with multi-modal support.

Pricing

• Pricing is not publicly available.

Aquarium

Aquarium is an ML data curation platform that helps you find and fix errors through interactive visualizations. It uses image embeddings to highlight issues, detect outliers, and identify model failures.

Aquarium

Key Features

• Supported Data Types: Aquarium supports image data for classification, segmentation, and 2D object detection tasks, as well as point cloud and sensor-based data for 3D object detection tasks.
• Collaboration: The platform lets you invite team members through its organization settings to collaborate on projects.
• User Interface: Aquarium features multiple data views with several display settings for extensive data exploration and analysis.
• Data Security: The solution complies with SOC 2 standards.

Other Features

• Query Bar: Users can quickly search data samples based on metadata, labels, and performance metrics.
• Automatic Insights: Aquarium automatically highlights problematic data samples based on performance metrics and embeddings.

Best For

• Teams are looking for a visualization platform to quickly identify data issues.

Pricing

• Aquarium offers Starter, Team, Business, and Enterprise packages.

Voxel51

FiftyOne by Voxel is an open-source CV modeling solution that helps you explore, curate, and identify data issues through aggregate metrics and visualizations. You can also use it to evaluate ML models.

FiftyOne

Key Features

• Supported Data Types: FiftyOne supports image, video, and geolocation data.
• Collaboration: FiftyOne Teams provides collaborative tools for working on shared datasets.
• User Interface: The tool offers an intuitive UI for searching relevant datasets and visualizing embeddings to identify critical data patterns.
• Data Security: Voxel51 complies with GDPR standards.

Other Features

• Aggregations: Users can aggregate data based on multiple statistical metrics, such as label counts, distribution, and ranges.
• Annotation Errors: The platform lets you quickly identify labeling errors through pre-trained models.
• Integrations: FiftyOne integrates with multiple labeling tools such as CVAT, Label Studio, and V7. It also supports modeling frameworks from HuggingFace and Ultralytics.

Best For

• Startups looking for a cost-effective solution for building high-quality training data.

Pricing

• FiftyOne is open-source. Pricing for FiftyOne Teams is not publicly available.

Arize

Arize is an ML observability platform that lets you monitor and evaluate CV, natural language processing (NLP), generative, and large language models.

Arize

Key Features

• Supported Data Types: Arize supports image, text, and time-series data.
• Collaboration: The platform features organizations and spaces, allowing members role-based access and shared resources.
• User Interface: Arize offers an intuitive UI with drag-and-drop features to upload and validate data files. 
• Data Security: The tool complies with SOC 2 Type 2 standards.

Other Features

• Task-based LLM Evaluation: Users can assess LLM performance based on specific tasks by analyzing hallucination, toxicity, and response relevance.
• Advanced Visualization: Arize features 2D and 3D Uniform Manifold Approximate and Projection (UMAP) views for model fine-tuning.

Best For

• Data and ML teams looking for an LLM-centric solution with advanced debugging features.

Pricing

• Arize offers Free, Pro, and Enterprise versions.

Hive

Hive is an AI platform consisting of content moderation models that classify explicit content in user applications. Using pre-trained algorithms, Hive also helps you build custom LLMs, text classification, and object detection models.

Hive

Key Features

• Supported Data Types: Hive supports image, audio, text, and video data.
• Collaboration: The platform lets you set read/write permissions for users according to the applications they are working on.
• User Interface: The platform offers an intuitive UI that guides you through the model development process.

Other Features

• Content Moderation: Users can include Hive models to tag harmful or explicit content in their applications.
• AutoML: Hive features pre-trained models that users can fine-tune according to their datasets.
• Model Evaluation: Hive features multiple metrics, such as precision, recall, specificity, etc., to assess model performance.
• Search: The platform includes advanced search functionality to find context-specific data samples.

Best For

• Teams that want to use readily available models to moderate content in their applications.

Pricing

• Pricing is not publicly available.

Arthur

Arthur is an end-to-end LLM development and deployment platform that lets you build, evaluate, and monitor AI applications with real-time security protocols. It offers four standalone products - Arthur Bench, Shield, Scope, and Chat.

Arthur AI

Key Features

• Supported Data Types: Arthur supports NLP, tabular, and CV data.
• Collaboration: It features collaborative tools through a centralized dashboard and customizable user permissions.
• User Interface: Arthur’s UI lets you quickly compare response results from different LLMs through intuitive visualizations.
• Data Security: Arthur complies with SOC 2 standards.

Other Features

• Real-time LLM Firewall: Arthur Shield monitors user prompts and LLM responses to detect and flag harmful content.
• LLM Chat: Arthur Chat provides a plug-and-play chat solution that you can integrate with your enterprise knowledge base.

Best For

• Teams looking for a solution to optimize enterprise-level AI applications.

Pricing

• Pricing is not publicly available.

Amazon SageMaker

Amazon SageMaker is a managed service that helps you develop, deploy, and monitor large-scale ML models. The platform lets you build foundation models from scratch and uses human-in-the-loop functionality to optimize model performance.

Amazon SageMaker

Key Features

• Supported Data Types: The platform supports image, text, video, geospatial, and point-cloud data.
• Collaboration: Data scientists, ML Engineers, and business analysts can collaborate on projects using Amazon SageMaker Studio Classic and Amazon SageMaker Canvas.
• User Interface: Amazon SageMaker Studio offers an intuitive interface to view model monitoring jobs and visualize performance through charts.
• Data Security: Amazon SageMaker benefits from AWS’s security compliance controls, which respect 143 standards, including GDPR and HIPAA.

Other Features

• Human-in-the-loop: The platform allows you to label data using human-in-the-loop methods.
• Feature Store: The Amazon SageMaker Feature Store lets you store, manage, and share relevant model features.
• Model Monitoring: The Amazon SageMaker Model Monitor lets you configure real-time monitoring jobs with alerts to notify you when it detects model deviations.
• Model Evaluation: SageMaker Clarify offers tools to evaluate LLMs, detect bias, and help with model explainability. 

Best For

• Large organizations seeking a platform to build AWS-based ML applications.

Pricing

• Amazon SageMaker has an on-demand pricing model that offers no minimum fees and no upfront commitments.

DeepChecks Alternatives: Key Takeaways

Investing in an ML platform is a long-term commitment that requires organizations to carefully weigh the pros and cons of each tool and select the most suitable option for their specific needs.

Below are a few key points to remember regarding ML platforms.

• Limitations of DeepChecks: Although DeepChecks offers various features for ML monitoring, its primary function is to optimize LLMs. Also, its lack of collaborative tools and security protocols makes it unsuitable for large-scale projects.
• Critical Factors to Consider: To choose the best option, users must assess a tool’s support for multimodal datasets, collaborative functionality, ease of use, and security compliance.
• Top Alternatives to DeepChecks: Encord, Voxel51, and Lightly are popular alternative solutions with the necessary factors for building complex AI systems.