How can you report data quality in automated machine learning?

1 Data quality assessment

Before you apply AutoML to your data, you need to assess its quality and identify any potential issues that might affect the model performance or validity. You can use various techniques to evaluate your data quality, such as data profiling, data auditing, data cleansing, and data validation. Data profiling involves summarizing the characteristics and distributions of your data, such as the number of records, missing values, outliers, and data types. Data auditing involves checking the accuracy and consistency of your data, such as the validity of values, formats, and relationships. Data cleansing involves correcting or removing any errors or anomalies in your data, such as duplicates, typos, or incorrect values. Data validation involves verifying that your data meets the requirements and expectations of your machine learning task, such as the target variable, the features, and the labels.

2 Data quality metrics

After you assess your data quality, you need to measure and report it using some quantitative metrics that can capture the different dimensions of your data. You can use various metrics to report your data quality, such as completeness, accuracy, consistency, timeliness, and relevance. Completeness measures the ratio of non-missing values to the total number of values in your data. Accuracy measures the degree of conformity of your data to the true or expected values. Consistency measures the degree of agreement or harmony among your data sources, attributes, or records. Timeliness measures the currency or freshness of your data in relation to your machine learning task. Relevance measures the degree of suitability or usefulness of your data for your machine learning task.

3 Data quality dashboard

One of the best ways to report your data quality in AutoML is to create a data quality dashboard that can visualize and summarize your data quality metrics and indicators. A data quality dashboard can help you communicate your data quality status and issues to your stakeholders, such as your clients, managers, or colleagues. You can use various tools and platforms to create a data quality dashboard, such as Python, R, Tableau, Power BI, or Google Data Studio. A data quality dashboard can include various elements, such as charts, tables, gauges, maps, or filters. A data quality dashboard can also include some features, such as interactivity, drill-down, or alerts.

4 Data quality feedback

Another important aspect of reporting your data quality in AutoML is to collect and incorporate feedback from your data consumers, such as your machine learning models, users, or applications. Data quality feedback can help you monitor and improve your data quality over time, as you can identify and address any gaps or problems that might arise from your data usage or changes. You can use various methods and sources to collect and incorporate data quality feedback, such as model performance metrics, user surveys, application logs, or data quality rules. Data quality feedback can also help you evaluate and compare your data quality across different data sets, models, or scenarios.

5 Data quality improvement

Finally, reporting your data quality in AutoML is not a one-time activity, but a continuous process that requires constant improvement and maintenance. You need to update and refine your data quality assessment, metrics, dashboard, and feedback as your data evolves and your machine learning needs change. You also need to implement some best practices and strategies to improve your data quality, such as data governance, data documentation, data lineage, data security, and data ethics. Data quality improvement can help you enhance your machine learning outcomes and value, as you can ensure that your data is reliable, relevant, and ready for AutoML.