Qxf2's AI/ML testing service

Many ML teams face challenges moving into production. Even when they succeed, models often fall short of customer expectations. For the few that do deploy a useful model, keeping it evolving and improving quickly becomes a struggle. Qxf2's AI/ML testing service is designed to solve these challenges.

Surprised that QA can help?

If you haven't worked with technical testers before, you might expect such complex technical issues to be beyond the scope of QA. But it's possible, and we've done it. Drawing on our experience with ML projects, we've developed a six-dimensional approach to help your team maintain model effectiveness and reliability over time. We also enable your data scientists and ML engineers to focus on tasks that best fit their expertise.

What you get

Your team (developers, data scientists) work with a QA engineer who is proficient in ML practices and has successfully managed multiple QA projects across various technologies. You benefit from data validation, insights into model decision-making, robustness evaluation against real-world scenarios, bias testing, metrics analysis, and intelligent model comparison. This approach will enhance your existing testing pipeline, accelerating model improvements and refinement.

ML Testing approach

There are many sources of error that can cause models to miss user expectations, leading to a wide range of testing needs. Over time, we've honed our approach to focus on these key areas. Not all of these will be applicable to your project, so we tailor our approach to meet your specific needs.

Qxf2 six-layered AIML testing offering helps data scientists and ML engineers to focus on tasks that best fit their expertise.
  1. Comprehensive Data Validation
    • What You Get: A detailed analysis and categorization of your data, with identification of potential issues that could impact model performance.
    • How We Do It: We employ both automated and manual checks, ensuring data quality and validating it against predefined schemas and metrics. Data is categorized into relevant sub-classes for targeted analysis.
  2. Robustness Evaluation
    • What You Get: Assurance that your model can handle adversarial examples, unexpected data deviations, and real-world failures, ensuring reliable performance under various conditions.
    • How We Do It: We generate synthetic data to simulate adversarial attacks and test the model under varied scenarios, simulating real-world conditions to challenge and verify its robustness.
  3. Advanced Metrics Evaluation
    • What You Get: Comprehensive insights into your model's performance using metrics such as F1 Score, Precision, Recall, AUC, RMSE, and more, giving you a complete understanding of your model's strengths and weaknesses.
    • How We Do It: We conduct a detailed evaluation using these advanced metrics, moving beyond simple accuracy to provide a nuanced analysis of your model's capabilities.
  4. Bias Analysis
    • What You Get: Identification of biases across dimensions like gender, race, and data distribution, with actionable recommendations to mitigate these biases.
    • How We Do It: We employ advanced metrics and scenario-based testing to uncover hidden biases, ensuring your model is fair and unbiased.
  5. Model Explainability Reports
    • What You Get: Clear explanations of how your model makes decisions, with insights into which features are most influential and why the model underperforms in specific scenarios.
    • How We Do It: Using tools like PDP, LIME, and SHAP, we create visualizations and reports that break down complex model behaviors into understandable insights, both at the global and local levels.
  6. Model Comparison Analysis
    • What You Get: A thorough comparison of your model against a baseline and across different versions, highlighting areas for improvement and tracking progress over time.
    • How We Do It: We conduct baseline comparisons and version-to-version analysis, using techniques like LLMs or ChatGPT, to ensure your model evolves and improves continuously.

Engagement Details

Our engagement process spans around 16 weeks, progressing through distinct phases tailored to your machine learning project. Each phase is designed to address specific aspects of model evaluation, from initial assessment to intensive testing, ensuring a comprehensive analysis and improvement of your model. Throughout, we work closely with you to refine and validate the model, ensuring it meets real-world requirements and maintains high performance over time.

AI/ML QA engagement. QA for ML done by technical testers at Qxf2.

Our Innovative Approaches in AI/ML Testing

Our approaches are based on both research and hands-on experience. We've implemented these techniques in our projects and research. You can read about our work and findings in our blog posts.

  1. Insights and strategies on testing Machine Learning Models
  2. Data quality matters when building and refining a Classification Model
  3. Baseline Model Comparison for Performance Evaluation
  4. Dataset and Model Evaluation using Deepchecks
  5. Robustness Testing of Machine Learning Models
  6. Metamorphic testing with SHAP Analysis
  7. Data Generation for Text Classification
  8. Build a semantic search tool using FAISS
  9. Testers, practice testing a machine learning algorithm!

Get in touch!

Want to help your ML team with this service? Write to Arun ([email protected]) or drop a note.

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