Peter-John Welcome GDE Firebase @pjapplez The life cycle of building Machine Learning enabled features on mobile An Introduction to ML life cycle on mobile 

Half screen photo slide if text is necessary Who I am Freelance Mobile Engineer Google Developer Expert for Firebase 

Data-driven vs ML-driven 

Data-driven vs ML-driven 

Data-driven vs ML-driven 

Will ML add unique value? 

Will ML add value Yes Developer could write a bunch of if statements or doing word searches but maintain that will become unmaintainable. 

● Recognize class entities ● Recommend content ● Natural language understanding ● Predicting future events (forecasting) ● Etc. https://pair.withgoogle.com/guidebook/ Examples of ML adding value 

● Automating high value tasks ● Providing static information to user. ● Providing transparency. ● Etc. https://pair.withgoogle.com/guidebook/ Examples of ML !adding value 

The ML life cycle 

ML Life Cycle 

ML Decision Pyramid https://www.youtube.com/watch?v=pm_-pVPvZ-4 

● Data wrangling. ● Making it accessible in some kind of database/warehouse/data lake. ● Trying to use the data to solve the problem. Data collection and labelling 

● Tensorflow ( Google’s open source ML platform) ● CoreML (Apples ML platform) ● Cloud-based approach. Training our model 

Training our model Tensorflow lite An open source machine learning platform for on-device inference Cloud , Google colab , on personal machine Python and Swift Has support for Android, iOS, IOT device etc https://medium.com/dvt-engineering/will-swift-take-over-python-as-the-go-to-language-for-ml-ff9f8d9d7141 https://www.tensorflow.org/lite 

Training our model CoreML Machine learning platform by Apple thats optimized for on device ML and leverages Apple hardware Supports only iOS devices CoreML does have a converter to convert Tensorflow and Pytorch models to coreML CreateML is an Xcode bundled ML tool to create coreml models https://developer.apple.com/machine-learning/core-ml/ 

Training our model Cloud based - API Google Cloud, Amazon, Azure all provide us the ability to train ML models in the cloud and deploy them. We could create a REST API or there are products that can generate REST API’s 

● Better accuracy ● Preventing Bias ● Feature importance Optimize 

● Tensorflow Lite Converter ● CoreML Converter import tensorflow as tf converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir) tflite_model = converter.convert() open("converted_model.tflite", "wb").write(tflite_model) https://www.tensorflow.org/lite/guide/get_started#2_convert_the_model_format https://coremltools.readme.io/docs Converting 

● Models are intellectual property ● Once the model is deployed its in the users hands. ● Obfuscation ● Model encryption ( weights, architecture, format scrambling) ● Hybrid processing ( processing data before inputting it into the model) Encrypt and Protect 

● On-Device models helps with privacy ● Faster inference/predictions ● Bloat app ( App size ) ● Updating model requires updating the app as its bundled. ● REST API can be used by then it requires network and violates user privacy ● Remote deployment of models with Firebase or ML deployment (iOS) Deployment 

● This is the hardest part on the process ● Analytics ● User Testing, A/B Testing ● UX is really important at this point in how the user interacts with the model ● Being aware of privacy when collecting data ● Making sure model is not Bias ● Explained really well in the People and AI guide book https://pair.withgoogle.com/guidebook/ Monitoring 

On-device ML Using transfer learning Being able to update/retraining parts a model This helps with personalization CoreML on iOS and Tensorflow lite on Android both support this. Great for privacy 

References ● https://www.tensorflow.org/lite ● https://developer.apple.com/machine-learning/core-ml/ ● https://pair.withgoogle.com/guidebook/ ● https://firebase.google.com/docs/ml/manage-hosted-models ● https://www.fritz.ai/ 

Peter-John Welcome GDE Firebase @pjapplez Thank You!