Google ML Kit aims to help developers add machine learning to their iOS apps
Google launched a new machine learning SDK called ML Kit, which provides a way for developers to add machine learning-based features to their Android and iOS apps, with Google's new framework following almost a year after Apple introduced the similar Core ML platform.
Introduced at Google I/O on Tuesday, ML Kit consists of a number of APIs developers can incorporate into their apps, with little prior knowledge of machine learning required to use them. The APIs are all provided by Google and have undergone extensive training, saving developers from building their own model and spending resources training it correectly.
The existing models offered by Google in ML Kit enable text recognition, face detection, labelling images, recognizing landmarks, and barcode scanning, all of which relying on imaging data from the device's camera. Future additions to the list include an API to add smart replies to messages, and a high-density face contour feature for the face detection API that will be useful for adding imaging effects to an image.
The ML Kit APIs are offered in two versions with certain tradeoffs. The cloud-based version does require an Internet connection, but offers high accuracy, while the on-device version is less accurate and depends on the device's processing power, but can be used offline.
For example, while the offline version is capable of identifying a dog within a photograph, it is unlikely to ascertain more specific details about the animal. Switching to the online version, the API would also be able to suggest what breed of dog is pictured.
While both API versions will be offered to developers for use, only the on-device version will be completely free. Developers opting to use the cloud-based APIs will end up needing to use Firebase, Google's mobile and web application platform, which will charge a fee.
Google is initially offering access to the APIs in a limited early preview, but has already provided documentation to start using ML Kit.
The cross-platform nature of ML Kit puts it in competition with Apple's own Core ML, a machine learning framework introduced at WWDC 2017. Similar in nature, the API can be used by developers to use machine learning to improve their apps, including a broad variety of model types, which take advantage of Apple's low-level technologies including Metal and Accelerate.
The initial APIs offered under Core ML included computer vision elements including face tracking and detection, landmarks, text detection, barcode detection, object tracking, and image registration. There are also natural language processing APIs available, which offer language identification, tokenization, lemmatization, and named entity recognition functions.
Introduced at Google I/O on Tuesday, ML Kit consists of a number of APIs developers can incorporate into their apps, with little prior knowledge of machine learning required to use them. The APIs are all provided by Google and have undergone extensive training, saving developers from building their own model and spending resources training it correectly.
The existing models offered by Google in ML Kit enable text recognition, face detection, labelling images, recognizing landmarks, and barcode scanning, all of which relying on imaging data from the device's camera. Future additions to the list include an API to add smart replies to messages, and a high-density face contour feature for the face detection API that will be useful for adding imaging effects to an image.
The ML Kit APIs are offered in two versions with certain tradeoffs. The cloud-based version does require an Internet connection, but offers high accuracy, while the on-device version is less accurate and depends on the device's processing power, but can be used offline.
For example, while the offline version is capable of identifying a dog within a photograph, it is unlikely to ascertain more specific details about the animal. Switching to the online version, the API would also be able to suggest what breed of dog is pictured.
While both API versions will be offered to developers for use, only the on-device version will be completely free. Developers opting to use the cloud-based APIs will end up needing to use Firebase, Google's mobile and web application platform, which will charge a fee.
Google is initially offering access to the APIs in a limited early preview, but has already provided documentation to start using ML Kit.
The cross-platform nature of ML Kit puts it in competition with Apple's own Core ML, a machine learning framework introduced at WWDC 2017. Similar in nature, the API can be used by developers to use machine learning to improve their apps, including a broad variety of model types, which take advantage of Apple's low-level technologies including Metal and Accelerate.
The initial APIs offered under Core ML included computer vision elements including face tracking and detection, landmarks, text detection, barcode detection, object tracking, and image registration. There are also natural language processing APIs available, which offer language identification, tokenization, lemmatization, and named entity recognition functions.
Comments
That is such a cliché.
You may never get the highest spec'd phone with an iPhone, but you'll definitely get the best performing one.
If Apple figures that they need to have more memory for ML, they'll add it.
ML poses some interesting challenges for google. They are under a bit of a magnifying glass these days, at least more than a couple years ago, in part due to all the crap facebook has been caught doing with their pants down, but also due to the rise of general awareness of data privacy issues. So google might very well face some adoption problems if they require all ML data to be processed on their cloud servers. What they've done here is strategic. Lure developers into the free offline capabilities, get them comfortable using google's ML tools, just like amazon has done with so many of their AWS tools. Then try to entice them to adopt the online tools over time as more people get dependent on the features they provide. It's not rocket science, this has been happening across the tech industry for the past 15+ years.
That's a great point, and I agree with your overall concern, but the same principles can also be applied to any number of apps that are gathering user data, and you don't get any say in the matter. Thankfully we do get a say with device sensors (camera, GPS, etc.) and some resources with public APIs (contacts, photos, etc.), but we don't get a lot of say in what kinds of usage data gets sent back to the developers or third-party analytics companies, and that's a huge problem. Flurry analytics (the google-analytics of mobile), for example, has their code embedded in over a million apps, and on over 2.6 billion devices worldwide ( http://flurrymobile.tumblr.com/ ). Do you get a say in whether or not your apps are sending data about your mobile device usage to Flurry? Not really. Not easily anyway.
In an ideal world, there would be "advanced user" settings or app where users could white-list or black-list any and all data that gets sent out of their devices, kind of like how Little Snitch works on Mac desktops and laptops. The problem is that this would likely be extremely complex and messy, and Apple generally tries to keep things as simple as possible, often at the expense of flexibility.
The issues are complex.
[realistic 'umm'] "AR Core and ML Kit work?”