This tool essentially democratizes machine learning by way of allowing anyone (who is able) to create custom models, but there is always a trade-off between simplicity and expressiveness. So, here is a short list of tools you may want to explore:

• Turi create: comes from a firm acquired by Apple in 2016; it provides tight integration with Core ML, allowing for easy deployment and custom models. It also provides a more comprehensive suite of machine learning models such as Style Transfer and segmentation. You can learn more about Turi create here: https://github.com/apple/turicreate.
• IBM Watson Services for Core ML: IBM Watson is IBM's AI platform, exposing an array of common machine learning models as a service. They have recently made available some of these services via Core ML models, allowing your application to leverage IBM Watson's services even when offline. 
• ML Kit: Google announced an ML Kit in early 2018 as a platform for common machine learning tasks such as image labeling and optical character recognition. The platform also takes care of model distribution, including custom ones.
• TensorFlowLite: A lightweight version of the popular machine learning framework TensorFlow. Like Core ML, it enables on-device inference.

These are only a few of the options available to integrate machine learning into your application, and all this is likely to grow significantly over the coming years. But, as we have seen throughout this book, the machine learning algorithm is (literally) only one part of the equation; data is what drives the experience, so I encourage you to seek out and experiment with new datasets to see what unique experiences you can come up with using what you have learnt here. 

Machine learning is evolving at an incredible pace. The website Arxiv is a popular repository for researchers to publish their papers; by just monitoring this site for over a week, you will be amazed and excited by the volume of papers being published and the advancements being made. 

But, right now, there is a gap between the research community and industry practitioners, which in part motivated me to write this book. I hope that what you have read in the pages of this book has given you enough intuition behind deep neural networks and, more importantly, sparked enough curiosity and excitement for you to continue exploring and experimenting. As I mentioned at the start of this chapter, we have just scratched the surface of what is currently out and possible, never mind what will be around in 12 months.

So, consider this as an invite or challenge to join me in creating the next generation of applications. I look forward to seeing what you create!