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Data quality is crucial for effective machine learning as poor data quality is a major obstacle in achieving accurate ML results. Deep checks, a Python package, offers comprehensive and programmable data inspection, fixing, and tracking for data scientists working on natural language processing models. By ensuring data quality throughout the ML workflow, from pre-training to post-production, deep checks helps improve model performance, reduces data labeling costs and procurement, and accelerates ML iterations.
Deep checks was developed to tackle the challenges faced by machine learning teams in ensuring their models behave as expected. With a lack of existing solutions, machine learning practitioners often struggle to validate and convince stakeholders about the accuracy and reliability of their models. Deep checks provides a testing framework that helps developers and users validate models, assess performance, analyze insights, and ensure continued functionality over time. It goes beyond explainability metrics, allowing comprehensive validation of probabilistic models and enabling continuous monitoring, auditing, and control throughout the ML lifecycle.
Deep checks offers users the flexibility to customize and extend the checks based on their specific use cases. The package provides a suite of pre-built checks for various aspects such as data integrity, model evaluations, performance analysis, and more. Users can also create their own checks and suites tailored to their needs. Deep checks supports tabular data and is expanding to include support for computer vision and NLP. It aims to cover a wide range of data types and machine learning frameworks, empowering data scientists to validate models efficiently and effectively.
Deep checks can be seamlessly integrated into the end-to-end machine learning workflow. It can be used during the training phase, allowing users to validate data quality, evaluate models, and establish performance baselines. By incorporating deep checks into continuous integration and continuous deployment pipelines, users can ensure the quality and reliability of models throughout production. The package also facilitates monitoring and auditing of ML models, enabling users to detect and address issues promptly. Deep checks serves as a valuable tool for ensuring the integrity and trustworthiness of machine learning systems.
Deep checks is continuously evolving to support a wide range of use cases, data types, and machine learning models. The team behind deep checks is working on expanding its capabilities to include additional domains such as computer vision, NLP, and time series data analysis. Integration with other ML tools and frameworks is also a focus, allowing users to leverage deep checks in conjunction with their existing workflows. The goal is to establish deep checks as a standard tool for data scientists, providing them with the necessary guardrails and insights to enhance the adoption and trustworthiness of machine learning models.
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