How to Ensure Data Quality for Machine Learning and AI Projects

 Data quality is an assessment whether the quality of data is fit for the purpose. It’s agreed that data quality is paramount for machine learning (ML) and high-quality training data ensures more accurate algorithms, productivity, and efficiency for machine learning and AI projects.

Why is Data Quality Important?

The power of machine learning is dramatically due to its capability to learn on its own automatically after being fed with huge amount of specific data. In this case, ML systems need to be trained with a set of high-quality data, as poor qualify data would mislead the results.

In his article, “Data Quality in the era of Artificial Intelligence” George Krasadakis, Senior Program Manager at Microsoft, puts it this way:”Data-intensive projects have a single point of failure: data quality.” He mentions that because data quality plays an essential role, his team at Microsoft starts every project with a data quality assessment.

The data quality can be measured from 5 aspects:

* Accuracy: how accurate a dataset is by comparing it against a known, trustworthy reference dataset. Robots, drones, or vehicles rely on accurate data to achieve higher levels of autonomy.

* Consistency: data needs to be consistent when the same data is located in different storage areas

* Completeness: the data should not have missing values or data records

* Timeless: the data should be up to date

* Integrity: high integrity data comforts to the syntax (format, type, range) of its definition provided by data model

Achieving the Data Quality Required for Machine Learning

Traditionally, data quality control mechanisms are based on user experience and data management experts. It is costly and time-consuming since human labor and training time are required to detect, review and intervene in sheer volumes of data.

Bytebridge.io, a blockchain-driven data company, substitutes the traditional model by an innovative and precise consensus algorithm mechanism.

Bytebridge.io, the data training platform, provides high-quality services to collect and annotate different types of data such as text, image, audio and video to accelerate the development of machine learning industry.

In order to reduce data training time and cost when dealing with complicated tasks, Bytebridge.io has built up the consensus algorithm rules to optimize the labelling system: before task distribution, set a consensus index, such as 80%, for a task. If 80% of the labelling’s results are basically the same, the system will consider they have reached a consensus. In this way, the platform can get a large amount of accurate data in a short time. If customers demand a higher accuracy of data annotation, they can use “multi-round consensus” to repeat tasks over again to improve the accuracy of final data delivery.

Consensus algorithm mechanism can not only guarantee the data quality in an efficient way but also save budget through cutting out the middlemen and optimizing the work process with AI technology.

Bytebridge’s easy-to-integrate API enables continuous feeding of high-quality data into machine learning system. Data can be processed 24/7 by the global partners, in-house experts and the AI technology.

Conclusion

In his Harvard Business Review, “If Your Data Is Bad, Your Machine Learning Tools Are Useless,” Thomas C. Redman sums up the current data quality challenge in this way:“Increasingly complex problems demand not just more data, but more diverse, comprehensive data. And with this comes more quality problems.”

Data matters, and it will continue to do so; the same goes for good data quality. Built for developers by developers, Bytebridge.io is dedicated to empowering machine learning revolution through its high-quality data service.