AI app development is the best way to create a competitive product. Here, we'll discuss 5 best practices that'll help you make it a success.
5 key skills for AI app development
AI app development is the best way to improve your software product.
But, before you go through with it, you (or your team) need to master some key skills to do it right.
If you’re a software engineer looking into developing AI apps but you don’t know where to begin, we’ve got you covered.
In this article, we’ll discuss 5 key skills you should learn for AI app development.
Table of Contents
AI and machine learning basics
To start, you need to be familiar with AI and machine learning (ML) basics.
Without them, you can’t hope to successfully develop an AI app.
Let’s start by defining and explaining the difference between:
- Machine learning
- Deep learning
The easiest way to think about AI is as the science of making computers think like humans.
And machine learning and deep learning are subfields of AI.
source: Singapore Computer Society
Machine learning develops systems that learn and improve based on patterns they observe in the data they’re trained on.
And they don’t need to be explicitly programmed to learn from that data.
Deep learning is a subfield of ML that uses artificial neural networks to mimic how the human brain works.
Next, you’ll need to know the basic approaches to training an AI model.
- Supervised learning
- Unsupervised learning
- Reinforcement learning
In supervised learning, the data the models are trained on is labeled and the model has access to the correct answers.
Supervised learning is the go-to training method if you need better accuracy.
On the other hand, unsupervised learning means that the models are trained on unlabeled data.
The goal of unsupervised learning is that the models find patterns in the data and learn on their own.
You can see a more detailed breakdown of the differences between them in the picture below:
The third basic model training approach is reinforcement learning.
In simple terms, reinforcement learning rewards desired behaviors and punishes undesired ones.
One use case of reinforcement learning are natural language processing (NLP) AI models like ChatGPT.
That’s why it’s a good idea to tell ChatGPT when you know it made a mistake – it’ll learn from that feedback.
Of course, we’ve just barely scratched the surface and it’s impossible to cover all the basics in a single article.
But, there are a number of AI and ML professional certifications and courses out there that’ll help you master the basics, such as:
- IBM’s Introduction to AI
- Microsoft’s Azure AI Fundamentals
- Google Cloud’s Machine Learning courses
- Wharton’s AI for Business
These courses will help you get started on your AI app development journey.
Data wrangling and preprocessing
High-quality data is the foundation of AI.
Developing an AI app with low-quality data is like building a house on sand.
Sure, it might look good at first glance, but it’s a disaster waiting to happen.
That’s why data wrangling and preprocessing are so important for a successful AI app development.
But, what exactly do these terms mean?
Data wrangling and data preprocessing are closely related processes.
But, data preprocessing comes first.
source: V7 Labs
Data preprocessing involves:
- Data cleaning
- Data transformation
- Data integration
- Data normalization
- Data smoothing
- Data reduction
In simple terms, the point of data preprocessing is to increase the quality of your data and make it more suitable for further use.
Additionally, data wrangling is the process of turning raw data into a usable format for AI models.
They’re both key skills you need to have to ensure you have high-quality data.
And there’s a good reason for that – poor data quality can cost you a lot of money.
A Gartner research survey showed that organizations believe that poor data quality is responsible for an average of $15 million per year in losses.
And with AI’s dependence on high-quality data, this is even more problematic.
That’s why you should use data preprocessing and data wrangling tools, such as:
All of these tools will help you increase your data quality and prepare it for use with AI models.
And that will increase your AI app development’s chances of success.
Knowing how to use AI frameworks
You likely won’t be building the AI model you’re going to integrate into your app from scratch.
And neither are most other companies.
That’s why knowing how to use AI frameworks is so important.
AI frameworks give you all the tools you need to:
- Build AI models
- Train AI models
- Deploy AI models
The most popular frameworks you should consider are:
Now, the framework you choose will depend on your specific business needs.
Google AutoML, for example, is a good choice if you don’t have a lot of experience with machine learning models.
That’s why it’s a good idea to use it if you want to get familiar with how AI models work.
It’s also good if you want to develop a simple AI app.
But, if your AI app is more complex, you’ll likely end up using TensorFlow or PyTorch.
You’ll be talking with our technology experts.
However, it’s mainly geared towards academic researchers, so it doesn’t have commercial support.
Still, it’s a good choice if you want to integrate a smaller-scale AI model or quickly build a prototype.
On the other hand, TensorFlow is better suited for more complex projects and for commercial use.
But, TensorFlow has a steeper learning curve.
Luckily, it also has an active community forum, so you can ask for help if you have any questions.
Knowing how to use these frameworks is essential if you’re developing an AI app.
Getting to grips with them will make integrating AI models much easier.
Integration and deployment skills
You can have the best AI model but it’s no good if you can’t deploy it in your app.
That’s why you need to have good integration and deployment skills.
Of course, your team’s likely already skilled at integrating and deploying new features in your product.
But, there are some AI-specific considerations you should keep in mind when developing an AI app.
To start, you need to make sure that your AI model is compatible with your existing systems.
This’ll make integrating your AI model much easier.
A good rule of thumb is to use your cloud computing provider’s AI platform.
You likely use cloud services from one of:
- Microsoft Azure
- Google Cloud AI
These platforms have a combined market share of 65% in Q2 of 2023.
And using their AI platforms to integrate and deploy AI models will make that process much easier.
Next, you should make sure the AI model you deploy can reliably scale.
According to Statista, scaling up tops the list of challenges organizations face when integrating AI into their product.
That’s why it’s important to plan for scalability before you even begin integrating AI into your product.
If you don’t, there’s a good chance you’ll hit a bottleneck at some point.
Also, make sure you continuously monitor and refine your AI model after integration.
Your initial deployment and integration can go well but there’s no guarantee your model won’t start drifting and lose accuracy.
In short, your team needs to pay attention to AI-specific integration and deployment challenges.
And if they do, they’ll master the skills they need to make the AI app you’re developing a success.
Resource management and optimization
It’s no secret that AI uses up a lot of resources.
And we’re not just talking about money.
AI models also take up a lot of computational resources like CPUs and GPUs.
That’s why you should close attention to managing your resources when developing an AI app.
And the best thing is, you can use AI tools to do just that.
The continuous monitoring tools we’ve talked about can help you monitor your AI model’s resource usage.
For example, Neptune has operational monitoring capabilities.
It allows you to monitor resource usage and make necessary changes to optimize it.
Another thing you need to consider is data storage.
AI models need a lot of high-quality data if you want them to deliver the most value to your business.
That’s why investing in effective data storage is so important.
It’s a good practice to do it even if you’re not developing an AI app, as global data volumes will continue to grow every year.
IDC estimates that by 2025 our global data volume will grow to 175 zettabytes – that’s 175 trillion gigabytes.
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So, what can you do to manage your data more efficiently?
The two key processes you should consider are data deduplication and data compression.
Data deduplication removes unnecessary copies of data and can free up a lot of storage capacity.
And data compression is self-explanatory.
It reduces the size of the data without sacrificing quality.
Both of these processes will optimize your data storage capacity.
In short, managing your resources effectively is key to your AI app’s success.
It’ll save you money and improve your AI model’s performance.
AI app development is the way to go if you want to take your product to the next level.
But, you need to do it right.
That’s why you need the 5 key skills we’ve covered in this article.
To recap, they are:
- AI and machine learning basics
- Data wrangling and preprocessing
- Knowing how to use AI frameworks
- Integration and deployment skills
- Resource management and optimization