Shaip

Sourcing datasets for artificial intelligence (AI) modules from public/open and free resources are among the most common questions we get asked during our consultation sessions. The entrepreneurs, AI specialists, and techpreneurs have expressed that their budget is a primary concern when deciding where to source their AI training data.    Most entrepreneurs understand the importance of quality and contextual training data for their modules. They realize the difference that relevant data can bring to outcomes and results; however, in many cases, their budget restricts them from acquiring paid, outsourced, or 3rd party training data from reliable vendors and resort to their own efforts in sourcing data. In this blog post, we will explore why you shouldn’t settle for public data resources to save money because of the consequences they will create. Reliable Publicly Available AI Training Data Sources  Before we get into public resources, the first option should be your...

The process of developing an artificial intelligence (AI) system is taxing. Even a simple AI module takes months of training to predict, process, or recommend an outcome. Successfully developing AI systems is challenging in terms of labor and time-consuming. Companies working within short timeframes could suffer significant losses if their training period extends past their deadline. Moreover, companies are also likely to feed their systems with bad data. Even if the deadlines are met, using low-quality AI training data will result in the actual cost of full-fledge AI development could end up being exorbitant. To avoid delayed training times and inaccurate results, a sophisticated strategy must be adequately implemented. We are going to cover a different aspect of the expenses involved in developing AI in this post. We’ve previously covered AI training budgeting; today, we will dive deeper and explore other costs involved in AI training data. Let’s get started. The True Costs of AI Tra...