How do you get deep learning to work in your business, product, or scientific study? The rise of highly scalable deep learning techniques is changing how you can best approach AI problems. This includes how you define your train/dev/test split, how you organize your data, how you should think through your search among promising model architectures, and even how you might develop new AI-enabled products. In this tutorial, you’ll learn about the emerging best practices in this nascent area. You’ll come away able to better organize your and your team’s work when developing deep learning applications.