Pipeline Probe: A Quality Analyzer for ML

Pipeline Probe is a tool designed for stakeholders in the machine learning (ML) industry, including vendors, maintainers, and businesses. It primarily assesses the quality of ML models within the MLOps process, inspecting generated artifacts and comparing results against predefined requiremetns. It integrates with MLOps pipelines, like Kubeflow, to automatically examine artifacts. A versatile plugin system allows it to utilize existing ML quality tests, ensuring a analysis of model quality, encompassing datasets, model architectures, performance metrics, evaluation results, and robustness.

Modular Plugin Architecture: Allows easy integration of various ML quality assessment tools. Each plugin operates as a separate program, enabling customization and scalability. Seamless MLOps Integration: Supports integration with pipelines like Kubeflow and MLflow tracking out of the box, facilitating automated artifact analysis during the ML lifecycle. Artifact Analysis: Automatically analyzes artifacts such as datasets, model architectures, parameters, performance metrics, and evaluation results. gRPC Communication: Utilizes gRPC calls for efficient communication between the core system and plugins, ensuring real-time status updates and management. Uses Rclone to mount remote filesystems, to save storage if a large dataset is used. Python code base, Kubernetes deployment. MIT License

Pipeline Compatibility: Requires MLOps pipelines that support plugin mechanisms; currently optimized for Kubeflow and MLflow and the Iml4e OSS Platform. Customization Needs: Additional development may be necessary to create plugins for unsupported pipelines or bespoke tools. Resource Allocation: Adequate computational resources are needed to perform intensive quality assessments and robustness evaluations. Security Considerations: Proper security measures must be in place when integrating with pipelines to protect sensitive data and models.

Machine Learning Vendors: Companies offering ML models and services seeking to ensure and demonstrate the quality and reliability of their products. MLOps Teams: Organizations maintaining ML pipelines and infrastructure that require automated quality assessment tools to streamline operations.

Access: Interested parties may request access to the Pipeline Probe for evaluation or integration purposes. Each request will be reviewed on a case-by-case basis to ensure alignment with project goals and confidentiality agreements. Licensing: Upon official release, the Pipeline Probe will be made available under the MIT License. This permissive open-source license allows users to freely use, modify, distribute, and sublicense the software, promoting widespread adoption and collaboration. Confidentiality: Until the official release, all users granted access are required to adhere to confidentiality agreements to protect proprietary information and intellectual property.