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Input Data Validation

Ensuring Quality: Comprehensive Input Data Validation QA for AI Systems

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For any AI System to perform successfully, the input data has to be free of any errors. Thus, the initial step for AI testing is checking the data. So, when it comes to the testing of AI system at Tx, our experts ensure the input data of an AI system is well scrubbed, cleaned and validated. We check the input data to be free of any kind of human bias or any kind of variation.

Input Data Validation Challenges and Solutions

Challenges
  • Ensuring all data points follow the same standards and eliminating the conflicting details
  • Obtaining accurate labels reflecting real-world conditions
  • Protecting data from unauthorized access and potential corruption
  • Implementing data governance frameworks that address data quality issues effectively
Solutions
  • Ensuring All Data Points Follow the Same Standards and Eliminating Conflicting Details
  • Obtaining Accurate Labels Reflecting Real-World Conditions
  • Protecting Data from Unauthorized Access and Potential Corruption
  • Implementing Data Governance Frameworks that Address Data Quality Issues Effectively

Benefits of Validated Data

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  • Ensure accurate results
  • Ensure data compatibility from various resources
  • Save time down the line
  • Avoid legal risks

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In your line of work, we know every minute matters.

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    How do we Do it?

    Collect Requirements

    Covering the basics, including schema, quality attributes, freshness, access, ownership, and more.

    Build the Pipeline

    Considering components beyond data quality, including how pipeline needs to be handled, idempotency, and data optimization.

    Sample the data, smoke test, data diff

    Run the pipeline on a small subset of data to identify any easy spots, inconsistencies, or errors.

    Write and implement data validation tests

    Ensuring schema continuity as a part of a data contract is another common data validation test.

    Continuously Improve and Deploy

    Checking back in with data consumers to gauge satisfaction levels and surface any additional requirements.

    Our Input Data Validation Differentiators

    Comprehensive Data Scrutiny

    Applying rigorous data quality checks that encompass accuracy, completeness, consistency, and timeliness

    Advanced Anomaly Detection

    Leveraging cutting-edge technologies, we identify and address anomalies in data sets

    Custom Data Cleansing Strategies

    Eliminate corrupt or irrelevant data, enhancing the efficiency and accuracy of your AI applications.

    Expertise in Data Compliance

    Adhere to relevant data protection regulations, reducing legal risks and building trust with your stakeholders

    Ongoing Data Validation Support

    Continuous data validation support to adapt to new data and evolving AI models