DeepChecks
Automates and monitors LLMs for quality, compliance, and performance.
Key Takeaways
- What it is
- Automates and monitors LLMs for quality, compliance, and performance.
- Best for
- LLM Evaluation
- ML Monitoring
- Open Source ML Testing
- Rating
- 4.5/5
What is DeepChecks?
As a seasoned reviewer of AI tools, you're likely to appreciate DeepChecks for its powerful and innovative approach to managing machine learning (ML) applications. Designed for developers, data scientists, and quality assurance teams, DeepChecks offers a comprehensive suite of checks and balances for LLM (Large Language Models) applications. The platform emphasizes maintaining control over the quality and compliance of generative AI, ensuring that each release meets the highest standards of performance and reliability.
DeepChecks stands out by offering a robust, open-source testing framework that supports a wide range of LLM applications. Its ability to automate and streamline the evaluation process of generative AI applications sets it apart from other tools in the market.
DeepChecks excels in providing a comprehensive and efficient toolkit for managing and deploying high-quality LLM applications. Its standout feature of automating the evaluation process not only saves time but also ensures adherence to the highest standards of quality and compliance, making it an invaluable tool for any organization working with generative AI.
Who is Using DeepChecks?
AI Researchers
Using the tool for developing and testing cutting-edge LLM applications.
Quality Assurance Teams
Ensuring that AI applications meet the highest standards of quality and compliance.
Data Scientists
Leveraging DeepChecks for ongoing monitoring and validation of machine learning models.
Software Developers
Integrating DeepChecks into their development pipelines for improved reliability and performance.
Uncommon Use Cases
Educational institutions for AI courses; AI ethics committees for compliance verification.
Key Features
Rating Breakdown
Pricing Plans
Free Trial
- Access a comprehensive free trial to explore DeepChecks' functionalities
Subscription Plans
- Detailed pricing information available upon request
Which DeepChecks plan should you pick?
Start with Basic for one AI application and limited evaluation volume; upgrade to Scale or Enterprise as application count, usage, support, or security requirements grow.
Basic
Good for small teams validating one LLM application with up to three seats and 5K DPUs per month.
Scale
Good for teams operating several production-grade AI applications that need five seats, higher capacity, premium support, and onboarding.
Enterprise
Good for organizations with high data volumes that need custom seats, custom application capacity, advanced security, and dedicated support.
Deepchecks on Sagemaker AI
Good for organizations wanting an AWS-managed deployment with data locality and native Amazon Bedrock integration.
Deepchecks Dedicated
Good for organizations needing single tenancy, region choice, on-premises or bare-metal deployment, custom SLA, or custom features.
DeepChecks Use Cases
Data integrity validation
Validate training or evaluation data for integrity issues before model development or deployment.
Outcome: Identify problematic data before it affects model results.
Train-test validation
Check data splits for drift, leakage, and other issues before training.
Outcome: Improve confidence that evaluation results are meaningful.
Model performance evaluation
Evaluate model performance across metrics and data segments after training.
Outcome: Find weak segments, bias, and performance problems.
Production model monitoring
Monitor models over time with checks, monitors, alert rules, and root-cause analysis.
Outcome: Detect and investigate production degradation.
LLM application evaluation
Upload evaluation or production interactions from Q&A, RAG, and multi-step applications for automated analysis.
Outcome: Gain visibility into LLM behavior and application quality.
Workflow Ideas with DeepChecks
Automated CI/CD model validation
- 1Validate training data integrity before model training
- 2Train the model and run performance checks
- 3Fail the pipeline or archive results when checks do not pass
Production monitoring and alerting
- 1Upload reference and production data
- 2Configure monitors and threshold-based alert rules
- 3Investigate alerts through dashboards, drill-downs, and test suites
LLM application evaluation
- 1Create an application and version
- 2Upload CSV or JSONL interactions through the UI or SDK
- 3Review automated properties, annotations, comparisons, and root-cause analysis
How DeepChecks fits into your stack
Python-Based Platform
Facilitates easy integration into existing Python workflows.
Extensive API Support
Enables custom integrations with various enterprise systems.
Community-Driven Enhancements
Regular updates and features driven by an active global community.
Multi-framework Compatibility
Supports various ML frameworks to cater to diverse development needs.
Pros
- +Streamlined Testing Process: Automates and simplifies the evaluation process, reducing the time and effort required for manual testing.
- +High Reliability: Systematically addresses potential errors and compliance issues both before and after deployment.
- +Community Support: Access to LLMOps.Space, a global community of LLM practitioners for collaboration and support.
- +Comprehensive Integration: Seamlessly integrates with over 300 open source projects, enhancing its utility.
Cons
- −Complexity for Beginners: The advanced features and systematic checks may present a learning curve for newcomers.
- −Resource Intensity: High-level functionalities might require substantial computational resources.
Limitations & when to consider alternatives
Known limitations
- Many drift checks require reference data. Reference data is optional, but many drift checks require it to compare production behavior against a baseline.
- Some checks require labels or predictions. Without labels or predictions, label drift, prediction drift, and performance metrics cannot run for the affected data.
- Completed LLM interactions are not fully mutable. Deepchecks does not permit changing data fields on completed interactions; the interaction must be deleted and re-uploaded.
- English-only properties have language constraints. English-only NLP properties do not work properly on non-English samples and should be used only when the data is English.
- Computer vision support has documented format requirements. Images, labels, predictions, and identifiers must follow Deepchecks' accepted VisionData formats.
Look elsewhere when
- You need a native mobile app
- You need a browser extension
- You need a desktop application installer
How to Use DeepChecks
- 1
Explore a variety of tutorials from basic setup to advanced features on the DeepChecks website and through community contributions on platforms like YouTube.
Who is DeepChecks For?
Small teams and startups
The Basic plan is positioned for small teams and startups validating LLM applications and early-stage agents.
Teams with production-grade AI applications
The Scale plan targets teams operating several production AI applications and multi-step agent workflows.
Companies with high data volumes
The Enterprise plan is designed for organizations needing custom capacity, advanced security, and enterprise support.
ML engineers and data scientists
Deepchecks provides Python packages, checks, suites, metrics, CI/CD workflows, and production monitoring for ML development teams.
Organizations with strict data residency requirements
VPC, on-premises, bare-metal, AWS-managed, and single-tenant deployment options address infrastructure and locality requirements.
Frequently Asked Questions
Which data and task types does Deepchecks support?
Deepchecks supports tabular ML tasks broadly. Regression, binary classification, and multiclass classification are supported for additional monitoring and performance checks.
Can reference data be provided without labels or predictions?
Yes, but checks such as label drift, prediction drift, and comparative performance metrics will not run.
Can production data be provided without labels or predictions?
Yes. However, label drift, prediction drift, and performance metrics are unavailable for periods without the corresponding labels or predictions.
What deployment options are available?
Deepchecks supports SaaS, open-source self-hosting, managed on-premises deployments, VPC, bare metal, and AWS-managed deployments.
Can data be uploaded through the user interface?
Yes. Data can be uploaded through the UI using CSV or JSONL files, or programmatically through the SDK.
How does Deepchecks support CI/CD?
It can validate data before training and validate model performance after training, with examples for Airflow and GitHub Actions.
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