The natural language processing, speech recognition, face detection, or future prediction are some of the features that now people expect when they use the app. Going ahead, the smart players such as Uber, Snapchat, and Allo have already started using the piece of technology to reshape the users’ lives with the improved user experience.
The user’s excitement for Apple’s Siri, Google Assistant, Microsoft Cortana, intelligent robots, Amazon Alexa and self-driving autonomous cars has drawn the businesses' attention towards the technology.
According to a research, the automation will increase the speed and accuracy of the decisions, and in the next three years, the investment in ML will nearly become double that’s equivalent to 64%.
In the US, enterprises have planned or in the phase planning different use cases embracing machine learning to get in pace with a major trend.
Let’s take a look at all the ML libraries that help you easily integrate the MI intelligence in the mobile app that’s on the meteoric rise:
Google cloud ML platform
If you are a rookie developer and wanted to integrate natural language processing, speech recognition, translation or face detection feature in your app, then Google machine learning cloud platform is your piece of cake.
To start with, you just need to create an account, get an API key for API request authentication and then you will become eligible to call the plethora of APIs in your Android app to add machine learning capabilities.
As of now, many Google APIs are in beta stage that may be deprecated at a later date, so the developers should be aware of using such APIs as they can impact the user experience after the app development.
Akin to the Google cloud platform, IBM Watson services also offer a range of APIs that you can call during the app development using the API key. To use the IBM Watson’s SDKs for Java, iOS, Python or Node.JS, you need to create a Bluemix account to obtain the service credentials and include cognitive computing in your mobile app.
In IBM Watson’s machine learning offerings include APIs for translation- from text to speech, visual recognition, personality insights, trade-off analytics, intelligent chatbots, and tone analyzers. Everything is ready at your hands, you can even use the APIs for the enterprise level. However, they are a bit costly, if used for the smaller projects.
The open-source ML library is programmed by Google’s brain team for the numerical computation using data flow graphs. The graph contains nodes which represent mathematical operations, while edges denote the multidimensional data arrays termed as tensors, that are communicated between the nodes.
Tensorflow lite is the light version of tensorflow software created for mobile devices to enable machine learning inference on them. Its flexible architecture makes it a great treat for everybody where the complex computations can be performed on several GPUs in any mobile using a single API. It offers numerous models such as NLP, word generation, text recognition, image captioning, image identification and computer vision that you can train and generate the custom model as per your needs.
Caffe2 is a deep learning framework whose modularity, lightweightness and scalability make it a perfect fit for the enterprises to create a prototype and then optimize the application at a later stage leveraging the power of GPUs in the cloud.
To quick start, the novice players can use the tutorial and either install the software on the PC or use cloud services to integrate the APIs offered by Caffe2 with Android studio, visual studio, and Xcode during mobile app development. Its applications include- translation, speech recognition, chatbots and computer vision.
Recently, the filters added by the Facebook’s app camera and Facebook messenger are the great instances, which are built using Caffe2 libraries.
The framework powered by Apple is used across Siri, Camera, QuickType in all the types of iOS devices. The wide array of trained models offered by CoreML for the various domains can be easily integrated into the app by just writing a few lines of code. It enables incorporating machine intelligence without impacting the app’s performance with less memory and less power consumption.
The CoreML service provides brilliant features such as text detection, barcode detection, image registration, face detection, face tracking, and pretty more. You can avail the CoreML’s pre-trained models using easy-to-learn tutorial.
Also read: The benefits of Integrating ML and ARKit to iOS platform
The Amazon products work well for both the beginners and the experienced developers create models leveraging visualization tools and wizards. The API-driven ML services help developers in adding intelligence to the mobile app with a diverse range of pre-trained models that offers conversational chatbot functionality, language analysis, computer vision and text to speech translation without reinventing the wheel.
There are tools in place like- Amazon SageMaker, and AWS DeepLens that enables swift training and deployment of the models. In addition, the broad framework compatibility, breadth of computing options for training and inference, deep platform integrations, data analytics and security services altogether are provided under the Amazon umbrella.
Microsoft cognitive services
Cognitive service integration with mobile apps means making the application that much powerful that it can smell out of your face, conversation or content that what are you upto. It’s not an over statement at all. The Microsoft services infuse the bots in your app with intelligent algorithms that can view, listen, understand and interpret what the users have a need for, just from the communication analysis.
There are some APIs integrating which the apps can be made smart to identify everything before the users speak. They are:
The Face API can detect the faces and compare them to find and match the exact face, which boosts up the security. The emotion API is able to track the facial expression to determine how the users are feeling during app usage. The content moderator API monitor and analyze the user-generated content at social channels, messaging platforms or gaming environment to filter the unobtrusive content. The computer vision API is capable of recognizing and extracting the content out of the images. The LUIS API helps in instilling NLP features in the mobile app.
Wrapping it up
“The global cognitive computing market is expected to reach $12.5 billion in 2019, up from 2.5 billion in 2014, at a CAGR of 38%,” as stated by Research and Markets.
The fact clearly indicate that the machine learning is the thing of past, present, and future of the technology world, that’s going to skyrocket without any fail. When we talk about the present, it’s an age of intelligence where intelligent apps are picking up the pace. Embracing machine learning trendin order to catch up is not ideal, instead, it must be done because it’s the demand of time, market, and the users.
Bringing machine intelligence to the mobile apps is not extremely taxing anymore. There are many machine learning tools and platforms available that aids in building the intelligent apps from the ground or helps in adding the machine intelligence capabilities to the existing apps.
We have shared a list of some APIs, tools or libraries that you have certainly explored to join the intelligent app trend race. If we have missed something important on the chart, do share with us in the comments below.