Machine Learning, AI and neural networks have so much to offer, with their idiosyncrasies in features that top AI companies need to figure out best deals from the roaster of offerings. As technology seeps into our daily lives, it affects the way we live, work, get entertained, eat, search, and do various other things. It has elevated from voice-powered personal assistants like Siri and Alexa to more complex underlying technologies involving suggestive searches, behavioral algorithms, applications with predictive capabilities, autonomously-powered self-driving vehicles, and many more general-purpose applications like Tesla, Cogito, Boxever, John Paul, Amazon.com, Netflix, Pandora, Nest, etc.
A true-artificial intelligent system is capable of learning on its own. These are from the likes of neural networks like Google’s Deep-Mind capable of making connections and reach meanings without relying on pre-defined behavioral algorithms. Implementation of AI into mobile applications can improve past iterations, letting users get smarter, becoming more aware, and allowing to enhance its capabilities and its knowledge.
It’s arduous to think about an application without a database in the present-day context. Every sort of application – be it mobile, web or desktop relies on some kind of database. Irrespective of the file structure, database, whether small or large is an essential part of every mobile app.
Basic Steps to Implement AI into Mobile App
Every mobile application will depend upon some form of Artificial Intelligence (AI) in coming times. AI can be enabled in enterprise applications in three basic ways:
STEP 1: Most prevalent and non-disruptive way to get started with AI is to integrate API’s into existing applications by language understanding, image pattern recognition, speech to text, text to speech, natural language processing, video search API’s, etc.
An example is the random sampling of all the inbound calls received by customer care representatives within the customer care cell. A supervisor routinely listens to all the calls to check the quality and overall satisfaction level of the customers. With limited time to analyze each call, the supervisor is normally left with very less time to assess the quality of calls, escalate the incidents to respective departments and handle unhappy customers as well as rude call-center agents. This scenario prevails in all industry verticals like banking, finance, insurance, online shopping malls, etc. Implementation of chat-bots (AI enabled voice-assistants) tremendously help customers in getting the desired solution within the stipulated time. Multiple AI platforms expose simple API at affordable price points.
- Amazon AI Services
- Google Cloud ML Services
- IBM Watson Services
- Microsoft Cognitive Services
- Vize.it
- Algorithima
- Clarifai
- AIception
- Lexalytics
STEP 2: Integrating API in enterprise applications can be a decent start of making use of AI into the applications, but it often remains concentrated on enterprise apps. Major sub-steps that must be followed can be to acquire data from various existing sources, implement a custom machine learning model, create data processing pipeline, identify the right algorithms, train and test the machine learning models and deploy them in production.
Machine Learning as a service offering takes the data and exposes the final model as an API endpoint by making use of cloud infrastructure for training and testing the models. This is the initiation of the requirement for the enterprises to start investing in a data engineering and data science teams. This will in-turn allow the customers to spin up infrastructure powered by advanced hardware configuration based on GPU’s and FPGA’s
STEP 3: Third and final step in AI implementation involves running open-source AI platforms on-premises. This calls for investment in infrastructure and teams to generate and run the models locally. Enterprise applications come with a high degree of customization. These are particularly suited for those who have to comply with certain policies before sharing confidential customer data.
As machine-learning-as-a-service (MaaS) is similar to platform-as-a-service (PaaS), running AI infrastructure is similar to a private cloud. Major cloud computing companies like Amazon, IBM, Microsoft, and Google offer facilities to construct and run neural networks, machine learning and other types of AI in their public cloud computing facilities. Their pricing is decided by the variety of tools that they use. Another class of service is provided by the cloud SaaS, Oracle, and Salesforce. Here is a list of open-source platforms for machine learning and deep learning especially for those who wish to implement the AI infrastructure:
- MXNet
- Microsoft Cognitive Toolkit
- Tensorflow
- Theano
- Caffe
- Torch
Additional Steps in AI Implementation in Mobile Apps
- Understand the features and need to add AI into the mobile app
- Mark the app area where AI can help improve the app
- Estimate the cost required
- Check feasibility of the MVP and practical changes that are required
- Involve ML-AI experts to design strategy
- Implement data and security features into the app
- Make use of robust supporting technological tools like storage aids, security tools, backup software, optimization solutions
All in All
AI has evolved to become the core building block of contemporary applications. Being as common and as important as a database, AI has created the road-map for building intelligent applications. Top app developers need to be able to read the landscape, stay prepared to move quickly to implement changes to minimize the chances of disruption moving forward. Machine learning, artificial intelligence, and automation have enhanced the opportunities for similar reasons. Exploring AI API must be the first step besides hosted MLaaS offerings. Insurance and finance companies are focusing on investing in AI companies that are at the forefront of creating new and interesting technologies that meet existing business needs.
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