A growing number of companies are seeking to apply artificial intelligence (AI) solutions, whether they want to launch disruptive products or innovate the customer experience. No matter how business is approaching their strategy, they’ll need to label massive amounts of data – text, images, audio, and/or video – to create training data for their machine learning (ML) models.
Of course, AI isn’t developed with a one-size-fits-all approach. We find that companies apply different strategies based on their size and stage of growth. Over the past decade, we’ve seen companies leverage AI solutions and encounter challenges along the way, as they come to us for data labeling, or the data enrichment and annotation that is required for training, testing, and validating their initial ML models and for maintaining their models in production.
As companies of all sizes seek to apply AI solutions, the one component that is more important now than ever is the role people play in the process. Data preparation is a detailed, time-consuming task, so rather than using some of their most expensive resources – data scientists – a growing number of companies are using other in-house staff, freelancers, contractors and crowdsourcing to get this massive amount of data work done.
BEST PRACTICES FOR AI SOLUTIONS IMPLEMENTATION
At the end of the day, it takes smart machines and skilled humans in the loop to ensure the high-quality data that performant AI models require. That’s a crucial dynamic when you consider some of the real-world challenges the technology is in a position to help solve. From the ability to identify counterfeit goods or reduce vulnerability to phishing attacks, to training autonomous vehicles with hardware upgrades that make them safer, it’s quality data that makes AI truly valuable.
詳見全文Full Text： DATACONOMY
若喜歡本文，請關注我們的臉書 Please Like our Facebook Page： Big Data In Finance