Shaip

 Today a business without Artificial Intelligence (AI) and Machine Learning (ML) is at a significant competitive disadvantage. From supporting and optimizing backend processes and workflows to elevating user experience through recommendation engines, and automation, AI adoption is inevitable and essential to survival in 2021. However, getting to a point where AI delivers seamless and accurate results is challenging. Proper implementation isn’t achieved overnight, it is a long-term process that can continue for months. The longer the AI training period, the more precise the results. With that said, a longer AI training duration demands more volumes of relevant and contextual datasets. From a business perspective, it is near impossible that you will have a perennial source of relevant datasets unless your internal systems are highly efficient. Most businesses must rely on external sources like third-party vendor

Quality data translates to success stories while poor data quality makes for a good case study. Some of the most impactful case studies on AI functionality have stemmed from a lack of quality datasets. While companies are all excited and ambitious about their AI ventures and products, the excitement doesn’t reflect on data collection and training practices. With more focus on output than training, several businesses end up delaying their time to market, losing funding, or even pulling down their shutters for eternity. Read More : -  https://www.computertechreviews.com/data-acquisition-considerations-you-cannot-overlook-in-2021/