Reduce Machine Learning Project Failure: 3 Tips

As machine learning revolutionizes the way companies do business, leaders are increasingly turning to machine learning to guide strategic decision making, from fraud detection to customer retention. Unfortunately, like many new and complex technology implementations, machine learning success can be elusive. Gartner reports that up to 85 percent of AI projects cannot deliver as promised. Many factors contribute to why machine learning projects fail, stall, or never come to fruition. Common pitfalls include overly ambitious objectives, using wrong or insufficient data, and neglecting to collaborate between business and project teams.