7 Tips for Machine Learning Success

The first part of our Business Guide to Machine Learning (ML) broke down how the umbrella concept of ML is far more nuanced in a business environment. The most effective strategies look at ML in a practical sense, employing both complex deep learning and less-intensive "cheap learning" techniques to optimize enterprise processes and gain tangible business intelligence (BI) insights. The goal of deploying ML within your business applications is to improve your bottom line or press your company's competitive advantage. But in the larger scheme of your organization, making the most of the time and resources you invest in this process goes far beyond the algorithms. The IT decision-makers in your business need to make sure everything factoring into your ML impementation--from the data and logistics to how you're engaging with users--works cohesively together to maximize effectiveness.