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Lakera's MLTest finds critical performance vulnerabilities in computer vision systems and allows you to easily fix them before they enter operation.

You can think of MLTest as your trusted safety expert that knows how to ensure that your models work as expected once deployed.

MLTest completely automates testing of computer vision systems - something that has previously required expert knowledge and significant resources.

So what are the main benefits of using MLTest?

• You automate testing and understand where your models are likely to fail at close to zero overhead.
• You know exactly which issues to focus on, which to fix next, thus significantly reducing iteration time.
• You don't have to set up complex testing infrastructure as MLTest provides that out of the box, meaning you can focus on what really matters and develop faster.
• You can showcase progress to team members, management, and customers easily.

MLTest is designed privacy-first. This means that while it's super easy to set up and configure, it completely runs on-premise – your data and models never leave your infrastructure.

MLTest finds systematic performance drops and robustness issues in your models. It can run the following tests:

• Stress tests: Automatically profile your models against real-world variations in the input, detect (un)stable input regions of your model and highlights them to you.

• Failure clustering: MLTest automatically identifies and clusters failure cases of your models to highlight systematic performance drops.

• Performance unit tests: These tests detects and highlight slices of your dataset where the model exhibits lower performance than expected.

• Metadata unit tests: Ensure that models never drop below a certain performance threshold for dataset slices specified through metadata.

• Robustness unit tests: These tests generate additional synthetic data to test your model against real-world variations in the input.

💡 We are constantly extending MLTest's model capabilities. Have a particular need? Get in touch with us! 😀

No model evaluation is complete without taking a look at the data as well. MLTest can run the following data tests:

• Representativity tests: MLTest automatically identifies and clusters underrepresentative data slices and biases to guide your data collection efforts.

• Metadata representativity tests: Identify underrepresentative data slices and biases based on metadata.

• Shortcut tests: Extract signals from your datasets that may lead to shortcut learnings.

💡 We are constantly extending MLTest's data capabilities. Have a particular need? Get in touch with us! 😀

MLTest has a convenient dashboard to see test results at a glance and debug model behavior efficiently. It also has model comparison features to understand how models differ between each other.

You can see a sample dashboard here.

MLTest is often used as part of a training-evaluation loop. It comes with a simple programmatic Python API which makes integration easy.

The following provides an example:

from lakera.mltest import Evaluator

...

evaluator = Evaluator(PATH_TO_OUTPUT)

evaluator.load_dataset("coco")
evaluator.load_model("my_yolo_model")
evaluator.load_default_config()

# You're all setup, simply execute the evaluation. 
evaluator.run()

MLTest completely automates testing as part of existing CI/CD pipeliens. You can think of MLTest as a quality gate that absorbs the complexity of testing while you focus on building the best-possible models.

To achieve that, MLTest can easily be packaged as a Docker container and can run as part of CI/CD pipelines (GitLab CI/CD, GitHub Actions, etc.).

For example:

stages:
  - test

test_object_detector:
  stage: test
  image: registry.gitlab.com/lakeraai/lakera/mltest:latest
  script:
    - python run_mltest.py --base_path $CI_PROJECT_DIR --test_output_path $CI_PROJECT_DIR/test_output
  rules:
    ...

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