Software applications make a substantial contribution to the normal activities of businesses. The use of software in business has increased from looking at products on the internet to sending emails to clients and coworkers. Integrating software applications and AI development services is a complicated process. It also necessitates concept generation, product definition, strategic design, coding, quality assurance, and other various factors.
Furthermore, if any stage in the development process fails, the entire process must be reset. Because the development process of your software applications is integrated into your industry’s ongoing and upcoming trends, there is no tolerance for distraction. Several companies are using artificial intelligence (AI) and machine learning (ML) for improved and better decisions to address the obstacles involved with the conventional software development process.
Though technology is still in its infancy, it improves the efficiency and accuracy of mobile app development tools integrated with AI. Below is a list of trending AI development tools for your business’s efficiencies.
6 development tools for mobile apps
This is a lightweight, adaptable, and scalable deep learning framework developed by Facebook. It is the continuation of the Caffe project, which began at the University of California, Berkeley. It is designed particularly for production use cases and mobile development. It provides developers with additional freedom in designing high-performance applications.
Caffe2‘s goal is to make it simple to experiment with deep learning and to capitalize on community contributions of new models and algorithms. For mobile development, it is cross-platform and interacts with Visual Studio, Android Studio, and XCode.
Its core C++ libraries provide speed and portability, while its Python and C++ APIs simplify prototyping, training, and deploying models.
- Make automation possible.
- Image manipulation
- Carry out object detection
- Operations using statistics and mathematics
- Distributed training is supported, allowing for rapid scaling up or down.
Facebook uses Caffe2 to assist its engineers and researchers in training big machine learning models and delivering AI on mobile devices. They considerably enhanced the efficiency and quality of machine translation systems by using Caffe2. Consequently, for all languages, all machine translation models at Facebook have been migrated from phrase-based systems to neural models.
This development tool stands for Open-Source Computer Vision Library, a library of programming functions for real-time computer vision and machine learning. It supports Windows, Linux, Mac OS, iOS, and Android and includes C++, Python, and Java interfaces. It also supports the TensorFlow and Poarch deep learning frameworks. The library, which is written in C/C++, may use multi-core processing.
OpenCV’s goal is to offer a standard infrastructure for computer vision applications and speed up the incorporation of machine perception into commercial goods.
These algorithms can be applied to the following tasks:
- Face detection and recognition
- Recognize items
- Recognize and categorize human activities in videos
- Keep an eye on camera motions and moving objects.
- Obtain 3D models of items.
- Create 3D point clouds with stereo cameras.
- Combine pictures to create a high-resolution image of a whole scene.
- Look for comparable photographs in an image database.
Pickers is a free assessment application that allows you to poll your class without the need for student devices. Its graphics and video SDK is OpenCV. Simply hand out paper clickers to each student and use your iPhone/iPad to scan them for fast checks-for-understanding, exit tickets, and spontaneous polls.
This development tool is an open-source software library that allows you to create machine learning models. Its adaptable design enables simple model deployment across various platforms, from PCs to mobile and edge devices. TensorFlow currently offers two mobile-device deployment options for machine learning models: TensorFlow Mobile and TensorFlow Lite.
TensorFlow Lite is an enhanced version of TensorFlow Mobile that provides increased performance and a lower app footprint. Furthermore, it has fewer dependencies than TensorFlow Mobile, allowing it to be constructed and hosted on simpler, more limited device situations.
- Recognition of speech
- Recognition of images
- Localization of objects
- Recognition of gestures
- Optical character recognition
- Text categorization in translation
- Synthesis of voice
The Alibaba tech team uses TensorFlow Lite to develop and optimize speaker recognition on the client-side. This overcomes several of the server-side model’s frequent shortcomings, such as inadequate network connectivity, prolonged latency, and poor user experience.
Google uses TensorFlow for complex machine learning models such as Google Translate and Rank Brain.
4# Core ML
This is a machine learning framework for integrating machine learning models into iOS apps. It has Vision support for image analysis, Natural Language support for natural language processing, and Gameplay Kit support for testing learned decision trees.
Core ML is constructed on top of the low-level APIs listed below, offering a simple higher-level abstraction to these:
- Accelerate improves the performance of large-scale mathematical computations and image processing.
- Basic neural network subroutines (BNNS) are functions that may be used to create and execute neural networks trained on previously collected data.
- Metal Performance Shaders is a set of highly optimized computer and graphic shaders that are meant to be quickly and effectively integrated into your Metal project.
The Create ML framework may also be used to train and deploy bespoke models. It is a Swift machine learning framework for training models with native Apple technologies such as Swift, XCode, and other Apple frameworks.
- Face and facial landmark recognition
- Text recognition
- Recognizing barcodes
- Language and script recognition Image registration
- Create games using a reusable and practical architecture.
Lumina is a Swift-designed camera that allows for the easy integration of Core ML models, image streaming, QR/Barcode detection, and a variety of other capabilities.
5# Cognitive Services
This is a set of APIs, SDKs, and services that allow developers to quickly add cognitive capabilities to their apps, such as emotion and video detection, facial, speech, visual recognition, etc.
You don’t have to be a data scientist to make your systems more intelligent and interesting. The ready-made services include high-quality RESTful clever APIs for the following:
- Make your apps capable of identifying and analyzing content within photos and videos. Image categorization, optical character recognition in pictures, face detection, person identification, and emotion identification are among the skills provided.
- Text-to-speech, speech-to-text, speaker recognition, and voice translation are examples of speech processing features that may be integrated into your app or service.
- Language: Your application or service will comprehend the meaning of the unstructured text or the purpose underlying a speaker’s utterances. It includes text sentiment analysis, keyword extraction, and automated and configurable text translation.
- Create knowledge-rich content that can be included in apps and services. It supports QnA extraction from unstructured text, knowledge base building from collections of Q&As, and semantic matching for knowledge bases.
- Search: Using the Search API, you can search across billions of web pages to discover precisely what you’re searching for. It includes ad-free, secure, location-aware online search, Bing visual search, custom search engine building, and many others.
6# Watson by IBM
This is one of the greatest AI-driven software development tools for companies looking for faster and better outcomes. Watson is pre-integrated and pre-trained on a flexible information architecture that has been built to expedite AI creation and deployment.
It aids organizations in making more accurate forecasts, automating operations, interacting with users and consumers, and augment knowledge. It includes developer tools that make incorporating chat, language, and search into your apps simple.
Watson provides the customer with precise developer resources for, among other things, speedier documentation, improved R&D, enhanced interactions, anticipating market trends, and minimizing risks.
These are some of the technologies that will assist you in incorporating intelligence into your apps. These libraries make it easy to add features such as speech recognition, natural language processing, computer vision, and many more, providing consumers with the wow factor of doing something that wasn’t previously possible.
Along with selecting the correct AI technology, you must also consider other elements that might impact your app’s performance. These criteria include, among others, the quality of your machine learning model, which may be impacted by bias and variance, the use of appropriate datasets for training, smooth user interaction, and resource optimization.
While developing any intelligent software, bear in mind that the AI development in your app is solving a problem and does not exist simply because it is nice. Thinking from the user’s point of view will assist you in determining the significance of a given issue. A great AI software will help users accomplish things quicker and allow them to achieve things they couldn’t do before.
With the rising popularity of intelligent apps and the need to accelerate their app development services, numerous organizations, ranging from large IT behemoths to startups, offer AI solutions. More developer tools will undoubtedly enter the market in the future, making AI in apps the standard.
Jignen Pandya has been working with Expert App Devs, an India-based professional mobile app development company. Expert App Devs provide end-to-end mobile and IoT solutions per the client’s business requirements.