Optimize your deep learning training process by understanding and tuning data loading from disk to GPU memory Photo by David Lázaro on Unsplash Deep learning experimentation speed is important for delivering high-quality solutions on time.

image credit Why you should read this post Deploying machine learning models to production in order to perform inference, i.e. predict results on new data points, has proved to be a confusing and risky area of engineering.

Google Health’s recent paper provides a fresh set of insights about what it takes to bring an AI solution to real clinical use. Below is a summary of some key takeaways which should be applicable to many companies and solutions in this space