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May 21, 2020  The Azure Machine Learning studio is the top-level resource for the machine learning service. It provides a centralised place for data scientists and developers to work with all the artifacts for building, training and deploying machine learning models. Machine learning (ML) has never been easier to pick up, yet developers and companies are still reluctant to adopt it. There is a belief that only Big Data scientists with doctorates and top-tier mathematic skills could understand how to use machine learning, which is not the case at all. If you can make a REST.

Machine learning (ML) is a programming technique that provides your apps the ability to automatically learn and improve from experience without being explicitly programmed to do so. This is especially well-suited for apps that utilize unstructured data such as images and text, or problems with large number of parameters such as predicting the winning sports team.

Android supports a wide variety of machine learning tools and methods. Whether you’re an experienced Android developer, or just starting out, here are some ML resources to help you get the best results.

Design for Machine Learning

Similar to other technologies, applying machine learning as a solution requires product managers, designers and developers to work together to define product goals, design, build and iterate. Google has produced two guides in this area:

• The People + AI Guidebook provides best practices to help your team make human-centered AI product decisions.
• The The Material Design for Machine Learning spec contains a collection of design guidelines and patterns for machine learning-powered features such as object detection and barcode scanning.

Build and Train a Model

Machine learning requires a model that's trained to perform a particular task, like making a prediction, or classifying or recognizing some input. You can select (and possibly customize) an existing model, or build a model from scratch. Model creation and training can be done on a development machine, or using cloud infrastructure.

Explore pre-trained models

Pre-trained models are available in ML Kit and Google Cloud. Read more about them in the next section.

Learn how to create your own models with TensorFlow

For a deeper hands-on development experience, you can use these TensorFlow resources:

• The TensorFlow for Poets codelab shows how to customize a pre-trained image labelling model using transfer learning.

Inference

Inference is the process of using a machine learning model that has already been trained to perform a specific task.

A key decision you’ll face as an Android developer is whether inferencing runs on the device, or uses a cloud service that's accessed remotely. Here are some of the issues you should take into account when making this decision:

Issue	On-device inference	Cloud-based inference
Latency	Lower latency enhances the realtime experience	Asynchronous communication and available bandwidth can affect latency
Resources	The particular device's resources, like processing power and storage, can limit performance	Cloud-based resources are more powerful and storage is more plentiful
Offline/Online	The ability to operate offline is a plus for running with poor or non-existing network infrastructure	A network connection is required
Cost	Battery usage, model download time for end users	Bandwidth for data transfer for end users, computing charges for developers
Privacy	User data never leaves the device	Data may leave the device, additional precautions may be necessary

The following table shows the available development options for each kind of inference:

On-device inference	Cloud-based inference
ML Kit TensorFlow Lite Magazines on kindle mac app. TensorFlow Lite can be used to deliver a trained TensorFlow model as an on-device solution: AutoML Use AutoML to train your own custom vision model on Google Cloud and run the resulting model on Android and other edge devices:	ML Kit Google Cloud APIs Cloud Video Intelligence - Labelling, scenes and explicit content detection Cloud Vision - Labelling, OCR, explicit content detection Cloud Natural Language - extract entities, sentiments, syntax from text

Deployment

Deployment is the process of packaging and updating your ML model for use on Android when doing on-device inference. There are three options available:

Include the model with your Android app

Your model is deployed with your app like any other asset. Updates to the model require updating the app. There are two ways you can add a model to your app:

• Include the model in the app's APK file. This is the most basic option.
• Include the model in an Android App Bundle and use dynamic delivery.

Provide the model at runtime

This enables you to update your model independently of your app. This also makes A/B testing easier. You can serve your custom model using the ML Kit Custom Modelsfunction or host the model download with your own infrastructure.

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A combination of both

It is not unusual for developers to package an initial version of their model with their Android app so that user does not need to wait for their model to download while updating the model to a new version.

For selected pre-trained ML Kit models, namely text recognition and barcode scanning, developers can use the shared model provided by Google Play Services resulting in smaller APK sizes.

Developer Stories

Making the impossible possible

Adding ML to your Android app opens up a new way to build applications that were too difficult to get right in a wide variety of conditions (such as reliable barcode scanning) or that were not even previously possible (for example, image detection and text sentiment).

Lose It!

Lose It! is a weight loss calorie tracker app. It helps you lose weight by logging all the food you eat so you know how many calories you have consumed. Lose It! uses the ML Kit text recognition API to scan nutrition labels to pull in the data when users are entering a new food that isn’t in their library.

PlantVillage

PlantVillage helps farmers detect diseases in Cassava Plants. Penn State University and the International Institute of Tropical Agriculture uses their custom TensorFlow models running offline on mobile devices to help farmers detect early sign of plant diseases.

Fishbrain

The Fishbrain app provides local fishing maps, forecasts, and connects millions of anglers. Fishbrain uses ML Kit Custom Model to deliver updated custom TensorFlow Lite models.

Core ML is optimized for on-device performance of a broad variety of model types by leveraging Apple hardware and minimizing memory footprint and power consumption.

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Run models fully on-device

Core ML models run strictly on the user’s device and remove any need for a network connection, keeping your app responsive and your users’ data private.

Run advanced neural networks

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Core ML supports the latest models, such as cutting-edge neural networks designed to understand images, video, sound, and other rich media.

Deploy modelsNEW

With Core ML Model Deployment, you can easily distribute models to your app using CloudKit.

Convert models to Core ML

Models from libraries like TensorFlow or PyTorch can be converted to Core ML using Core ML Converters more easily than ever before.

Personalize models on-device

Models bundled in apps can be updated with user data on-device, helping models stay relevant to user behavior without compromising privacy.

Encrypt modelsNEW

Xcode supports model encryption enabling additional security for your machine learning models.

Create ML

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Build and train Core ML models right on your Mac with no code.

Core ML Converters

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Convert models from third-party training libraries into Core ML using the coremltools Python package.

Models

Get started with models from the research community that have been converted to Core ML.

Powerful Apple Silicon

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Core ML is designed to seamlessly take advantage of powerful hardware technology including CPU, GPU, and Neural Engine, in the most efficient way in order to maximize performance while minimizing memory and power consumption.