Why data remains the greatest challenge for machine learning projects

Join us on November 9 to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers at the Low-Code/No-Code Summit. Quality data is at the heart of the success of enterprise artificial intelligence (AI). And accordingly, it remains the main source of challenges for companies that want to apply machine learning (ML) in their applications and operations. The industry has made impressive advances in helping enterprises overcome the barriers to sourcing and preparing their data, according to Appen's latest State of AI Report. But there is still a lot more to be done at different levels, including organization structure and company policies. The enterprise AI life cycle can be divided into four stages: Data sourcing, data preparation, model testing and deployment, and model evaluation.