We have gone through mobile machine learning SDKs offered by Google—TensorFlow for mobile—and Apple—Core ML—in the previous chapters and got a good understanding of them. We looked at the basic architecture of those products, the key features they offer, and also tried a few tasks/programs using those SDKs. Based on what we have explored on the mobile machine learning frameworks and tools so far, we will be able to identify a few gaps that make it difficult to carry out mobile machine learning deployments and subsequent maintenance and support of those deployments. Let me list a few for you:

• Once we create the machine learning model and import it into the Android or iOS application, if there is any change that needs to be done to the model that was imported into the mobile application, how do you think this change will be implemented and upgraded to the application that is deployed and being used in the field? How is it possible to update/upgrade the model without redeploying the application in mobile application stores—the App Store or Play Store?
• Once the machine learning model is in the field and is being used by users in the field, how do we monitor the performance and usage of the model in real-time user scenarios?
• Also, you might have experienced that the process and mechanism to use the machine learning models in iOS and Android is not the same. Also, the mechanism to make the machine learning models created using a variety of machine learning frameworks, such as TensorFlow, and scikit-learn and, in order to make it compatible with TensorFlow Lite and Core ML is different. There is no common process and usage pattern that developers can follow to create and use these models across frameworks. We feel that if there was a common approach to use these machine learning models from different vendors using the same process and mechanism, it would be a lot more simple.

An attempt has been made by the Fritz platform to answer all the previously mentioned gaps observed in machine learning model usage and deployment. Fritz, as a machine learning platform, tries to provide solutions to facilitate machine learning model usage and deployment for mobile applications. It is a mobile machine learning platform with ready-to-use machine learning features, along with options to import and use custom ML models—TensorFlow for mobile and Core ML models.

So, in this chapter, we will be going through the following in detail:

• Understanding the Fritz mobile machine learning platform, its features, and its advantages.
• Exploring Fritz and implementing an iOS mobile application by using the regression model we already created using Core ML.
• Exploring Fritz and implementing an Android mobile application by using the sample Android model we created in Chapter 3, Random Forest on iOS, using TensorFlow for mobile.