Tips for How to Create an AI App for Your Business

6 min readApr 24, 2020

We are entering the age of “Software 2.0,” where artificial neural networks (ANN) are already in use and appreciated by those who are from a development background.

Even, there, however, technologies like artificial intelligence, deep learning, machine learning, and advanced analytics changing the way developers create intelligent software entities through computers and in collaboration with human intelligence.

Today all of the smartphones, smart TVs, cars, and video games use artificial intelligence. Like you can use Siri to give you directions to the nearest petrol pump.

Tesla is using AI and big data to make the idea of self-driving vehicles into reality. According to a post published in Fortune, AI can now read our thoughts and convert them to images by interpreting brain signals.

We can say AI is everywhere, and it’s making a huge impact on our business, in our lives every day, and adding potential the way we communicate using technology not human.

So, what are you planning for your AI project? Have you begun planning and coding? If yes, then take a look at my list of ideas before diving into an AI app development project of your own.

Tips and Best Practices for AI App Developers

For making an AI application, languages such as Python, Lisp, Java, and Prolog are the most popular.

In particular, Python is used as a base language for most AI-enabled applications and software because of its simple syntax, tons of libraries and tools, and operating systems support.

To successfully develop an AI application, I recommend several useful tools to make development easier. Simply familiarize yourself with libraries and tools like Django, Flask, NumPy, Matplotlib, wxWidgets, PyQt, OpenStack, Pandas, Scikit, Theano, AIMA, pyDatalog, SimpleAI, EasyAi, PyBrain, MDP, Scikit, PyML, and others.

Nowadays, it’s important for AI and ML developers to think up a catchy name for their application. Amazon came up with Alexa, Apple came up with Siri, and Google introduced Google Translate. So, think of a unique name for your AI app.

Next, select your IDEs and code editor with Python Support. You can go for Sublime Text 3, GNU Emacs, Eclipse+PyDev, Atom, Vi/Vim, Visual Studio IDE, or PyCharm IDE.

Sublime Text 3

For AI projects, I recommend Sublime Text 3 because it’s a feature-rich code editor with incredibly advanced features such as quick shortcuts/search, split ending, distraction-free writing mode, command palette, it supports all platforms, and many more reasons.

Now, make a Python file “xyz.py” on your desktop. If you’re using macOS, use terminal commands:

cd Desktop touch xyz.py

Open xyz.py file with your code editor and write the following command in your system:

Step 1

Import random

Step 2

answers = [ ‘I did not understand what you just said’, ‘It doesn\’t look like anything to me’, ‘I don\’t know, whatever’]

This is how you can start. Now come to the main part of adding loops to your coding.

while True: user_input = input (“>>>”) if user_input . lower() == ‘hi’ : print(“Hello”) else: print (random.choice(answers))

Requirements for Good AI Coding

Things that you really need in a coding environment vary from app to app. However, there is a core set of functions that makes your coding tasks easier. Here are the points you should consider for AI coding:

Examine Your Data

AI and ML models will reflect the way they are trained, so analyze your raw data over and over again to understand your input data as much as possible.

Examine your data for any mistake, missing values, or incorrect labels.

Suppose your app will be used for calculation of all ages, but you only have data for people of ages <=45 or if your app will be used for year-around holiday, but you only have data from winter. Make sure your data is accurate.

Apply a User-Centric Design Approach

Design your app with appropriate features for better clarity and control. Check to see if any features in your programming model are unnecessary or redundant. Try to use the simplest form of the model to meet your user’s preferences.

Use Python Dictionary to Better Code Your AI Application

Dictionary in Python is used to store data. It’s a set of key values, where each key is unique and provides a useful way to store data in Python.

Typically, the data stored in the Python Dictionary is related to the information contained in a User ID or User Profile.

It plays an important role in your AI app development for data storage. Let’s see the example of coding with too many if/else clauses:

if name == "Mary": print "This is Mary, she is a dancer" elif name == "Shaina": print "This is Shaina, she is an engineer" elif name == "Tim": print "This is Tim, he is a doctor"

With Python Dictionary, you can write the same code as:

name_job_dict = { "Mary": "This is Mary, she is a dancer", "Shaina": "This is Shaina, she is an engineer", "Tim": "This is Tim, he is a doctor", } print name_job_dict[name]

Machine Learning Tools to Consider

AI app developers should be ready to experiment with the new frameworks of machine learning and deep learning. I recommend Apple Core ML for AI development because it is a domain specific framework of machine learning.

It includes features like vision and image analysis, natural language processing, and GamelayKit to optimize and evaluate common behavior and decision trees such as random number generation, AI pathfinding, and more.

Caffe2 is also a preferred choice for the modular deep-learning open-source framework. It is good for developers because it will allow you to experiment with deep learning and machine learning models and algorithms.

It comes with C++ and Python APIs that allow developers to prototype immediately and optimize their app development process.

Google TensorFlow is also a good choice for deploying machine learning and AI applications on embedded devices. TensorFlow Lite allows developers to build apps with fewer dependencies and in a smaller binary size.

However, TensorFlow Lite is also available for the developers, but it does not cover all the use cases as the TensorFlow Mobile covers. For AI and ML app development, you should use TensorFlow Mobile.

Low Code Platform to Build Your AI Application

Mendix is the best AI-assisted low-code development platform for AI developers. It helps boost developers’ productivity with next-step recommendations and expert quality suggestions on the app quality and performance.

The low-code environment will benefit AI developers by providing a collaborative and intuitive development platform for AI apps, cloud-native architecture with best in class features, open and extensible APIs, model APIs, and SDKs for rich extensibility options, and you can build multiple apps without using multiple tools and code bases.

Mendix Assist is more like a training tool for developers that helps reduce the cost and time of re-work on your AI project, helps prevent issues at the time of app development, and helps you focus more on your task and business value.

So we have come to the conclusion that the development of AI apps is thrusting confidently forward. Indeed, the extent to which AI comes into businesses gives a lot of opportunities to people in the business industry.

Further, AI technologies in new mobile application development will give a new impetus to new opportunities, smart interaction, intellectual decision making, and personalization.

In this article, we tried to give you a couple of useful tips on how to build an intuitive AI app for your business firm, in particular for your future application. For more information on how AI and ML have revamped business applications read the complete information here.

Apart from these points, what you have to focus on is organizing the right development team for your AI project because app development is a team game! Your development team and each player must have a common goal; success! If you any information or ideas to share, kindly post them in the comments section below.