IBM Research Data Quality for AI API
The data preparation step is widely known to be as much as 80% of the total investment of time in developing machine learning models. To reduce both model costs and time to completion, it is necessary to reduce the anticipated labor by improving the quality of the data early in the development cycle.
Among other features, the toolkit notably provides functionality in two ways:
A) Data Quality Analysis Methods that return quality scores and insights on those quality scores, even pointing to specific regions of data responsible for reducing the score and recommending how such data regions can be improved
B) Data Remediation Methods that execute the recommendations provided by the quality analysis methods. The toolkit supports a variety of data types, including data tabular and time series data.
IBM Research Data Quality for AI API
The data preparation step is widely known to be as much as 80% of the total investment of time in developing machine learning models. To reduce both model costs and time to completion, it is necessary to reduce the anticipated labor by improving the quality of the data early in the development cycle.
Among other features, the toolkit notably provides functionality in two ways:
A) Data Quality Analysis Methods that return quality scores and insights on those quality scores, even pointing to specific regions of data responsible for reducing the score and recommending how such data regions can be improved
B) Data Remediation Methods that execute the recommendations provided by the quality analysis methods. The toolkit supports a variety of data types, including data tabular and time series data.