How Outsourcing Data Annotation can help ML Companies

Freeing machine learning professionals from mundane labelling and tagging tasks, outsourcing data annotation diverts quality attention on ML development, offers contextual flexibility and agile execution, thereby driving operational excellence required for building performance-driven AI ecosystems.Retail giant Walmart successfully cataloged 2.5 million items, 98% of their products, with help of a data annotation service provider that helped them with accurate training datasets for their AI/ML models.Enterprises spend five times more cost on internal data annotation as compared to what they incur when they outsource the activity. The high cost is due to lack of right expertise to tactically drive data annotation. Outsourced data annotation helps factor in resource shortage, coast and skewed timelines; thereby never making the ML application development come to standstill.Establishing a close loop between data annotators and machine learning engineers, data annotation executed in a seamless manner by data annotation service providers empowers you with complete, validated, error-free and refreshed training datasets for your artificial Intelligence and machine learning models.What are the challenges of in-house data annotation?To understand how outsourcing data annotation helps ML companies, let’s start by understanding the challenges of in-house data annotation.Driving quality data annotation vis-a-vis stringent machine learning model development deadlines creates a pressure ...


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