Applying AI in Software Development: Best Practices and Examples

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Diagram showing AI in software development

There’s no doubt that artificial intelligence has transformed the business world and that more changes are yet to come. In December 2022, the US government issued a report showing that nearly all industries have adopted AI in some fashion to improve their workflows. Looking to the future, the study concluded, “AI will also drastically change how we design our workplaces and companies' business models.”

Perhaps no career field has been more impacted by the rise of AI than software development. Developers are using AI and machine learning (ML) technology to automate manual tasks and optimize their finished products.

A wide variety of tools and methodologies that use AI and ML are available today to help your development team take their software to the next level. Here’s a look at some of the best ways to apply AI technology to your software development practices.

Here are five things to know about AI in software development:

  • AI is transforming industries, and software development has been significantly impacted by AI and ML technologies.
  • Integrating ML models into software development requires a shift from traditional software development lifecycle to a new, data-focused approach.
  • The ML development model has stages such as Goals Definition, Data Collection, Data Preparation, Model Learning, Model Deployment, and Model Management.
  • Best practices for using AI-powered development tools include using AutoML for training models, TensorFlow for deployment, and choosing ML lifecycle management solutions like Amazon SageMaker, Azure Machine Learning, or Google Cloud AI Platform.
  • Including APIs in AI software projects helps maximize the reach and potential uses of your AI-powered software, while API management solutions like DreamFactory streamline API management in the development process.

In this article we'll cover:

Integrating the ML Model into Software Development

The traditional software development lifecycle looks like this:

  • Requirements analysis: The end goal of the software, expected user requirements, and technical specs are all laid out at this stage.
  • Design: Developers and managers design how the software will look and work.
  • Development: Code is written and compiled into alpha and beta versions.
  • Quality assurance (QA): Thorough software testing is done, with the goal of certifying a beta version as a release candidate (RC).
  • Delivery: Once the bugs have been worked out of an RC, a release version is delivered.
  • Maintenance: Developers address user-reported bugs and work towards never versions of the software.

Development teams looking to incorporate AI into their projects may be surprised to find out that it’s not a matter of just using a decision-making algorithm or adding some predictive analytics APIs to the code base. Applying AI and ML to your projects means adopting a whole new software development model.

The machine learning development model completely rewrites this process. Whereas conventional software development focuses on features and capabilities, the ML model revolves around data. The information fed into a machine learning algorithm helps it learn and adapt to the needs of its users.

Think about the digital assistants you use. Amazon Alexa or Apple’s Siri take the data you give them—your voice commands—and learn from context. The assistants understand your instructions more precisely and learn new real-world concepts. Over time, interacting with Alexa or Siri is less like talking to a robot and more like interacting with a human.

This wouldn’t be possible with the traditional software development model. If you had to wait for a new version of the digital assistant to be shipped every 12 to 18 months, you’d quickly lose interest. But since the developers followed the ML development model, they’re constantly learning and improving.

Using the Machine Learning Development Model

Here’s an overview of the ML development model and how it’s different from the standard software lifecycle.

  • Goals Definition: Humans still need to define the problems they want software projects to solve. But once that’s complete, natural language processing is used to help ML understand the requirements.
  • Data Collection: This stage defines how the ML algorithm will collect and manage data. Collection mechanisms, data formats, and ranking the importance of information the algorithm will receive are all laid out at this stage.
  • Data Preparation: Once data formats and collection mechanisms are defined, the next step is to prepare the initial dataset. It’s important to keep in mind that the broad goal is to gather and prepare only the correct data. In a Big Data scenario, this can be challenging.
  • Model Learning: Now machine learning models learn from data rather than human instructions. This is an iterative process and results may be less predictable at first. 
  • Model Deployment: The model deployment and integration stage ensures that the model returns useful information. 
  • Model Management: This is the counterpart to the maintenance phase in traditional software development. But rather than developers debugging their shipped product, new code is developed by machine learning processing methods such as deep learning neural network weights.

It’s clear that the machine learning development model is substantially different from the old ways of creating software. However, a new model must be adopted to use the advancements that AI and ML offer programmers. 

Once your team grasps the process, many commercial and open-source AI projects can help your programmers integrate it into their workflow. Here's a look at some of the tools available—and the best practices for using them.

Best Practices for Using AI-Powered Development Tools

Streamlining the development process with AI and ML is easier than ever, thanks to a proliferation of development tools on the market today. Here’s a look at some of the most commonly used tools and how your team can benefit from them.

Use AutoML to Train Models

The vision of Google’s AutoML is to “train high-quality custom machine learning models with minimal effort and machine learning expertise.”

Since AutoML is designed for programmers who have limited machine learning experience, it’s become a popular tool for companies adapting to the ML development model. The data preparation stage can be achieved via a query from Google’s BigQuery platform or an upload of a comma-separated values (CSV).text file.

From there, training a model is done with a simple graphical interface. Developers can select from a library of training objectives or create custom training solutions. Training data is separated into training, validation, and test sets. This split can be done in several different fashions, including random assignment, manual assignment, and chronological assignment based on a selected column. Finally, a “training budget” defines the maximum number of node hours used for testing.

Once a model has been trained, AutoML offers many deployment and testing methods. A popular deployment method is to export to a saved TensorFlow model, which can then be served in a Docker container. The next section will tell you a bit about TensorFlow.

While a paid product from Google, AutoML has proven itself a worthy investment to many development teams. It allows for ML development model automation without relying on professionals with degrees in data science. That said, data scientists will also find AutoML to be one of the most powerful machine-learning frameworks on the market today.

Ease Deployment With TensorFlow

TensorFlow is another Google product for the ML software development process. Unlike AutoML, TensorFlow is open source and freely available for companies to deploy on their own hardware and cloud servers.

The primary purpose of TensorFlow is to help you implement best practices for model training, tracking, and retraining. Its flexibility has made it a popular choice for all kinds of development, from the web to mobile apps and in-house custom business apps.

TensorFlow is a comprehensive ML platform, with an emphasis on the model deployment and model management stages. As an open-source project, a large community contributes to freely available libraries suitable for all forms of application development projects.

Choose A ML Lifecycle Management Solution

The model management stage may be the most crucial step for AI in software development. Ensuring your models are continuing to learn and algorithms are improving can be difficult for dev teams to manage manually. Fortunately, there are several good ML lifecycle management platforms available to guide you along this process.

Some of the best available include:

Develop APIs for Your AI Software Projects

Some of the most successful AI-powered software projects offer a useful API for other apps to use. Sticking with Google’s AI/ML offerings, their text analysis API contributes to the AI software development community by offering natural language processing services.

Data sent to the text analysis API can be processed in several different ways that are helpful to DevOps teams looking to optimize their ML data. Features include syntax analysis (analyze text and find the key parts), entity analysis (find specific info in unstructured data), and sentiment analysis (identify tone and mood in written or spoken words).

Much like the web service REST APIs that enable developers to create new services and apps, APIs in AI software projects provide a platform for future innovation. You might choose to create a public API so that everyone can use your AI-powered software features—and discover new uses. Or you may need a private API for your internal systems to collect metrics and analytics data.

Whatever the use, including APIs in your projects will prove valuable down the road. Including API interfaces ensure that the software you spend time developing will be used by the largest audience possible. They may even help you find new uses for the smart AI and ML models you’ve developed.

How DreamFactory Manages APIs for Your AI Software Development

Once you’ve committed to using AI and ML in your software development process and include APIs in your project, API management becomes important. APIs are an essential part of the software ecosystem today and manual management is not a feasible solution.

Documenting your APIs, testing, deploying, and managing versions are incredibly time-consuming tasks. The optimizations that AI offers the software development process shouldn’t be slowed down by tending to manual API management. That’s why you need a comprehensive API management solution.

DreamFactory’s iPaaS API management platform gives you everything you need. Every aspect of the API lifecycle is covered—from no-code API generation and documentation all the way through to version management. To see how DreamFactory can streamline your API management, sign up today for your 14-day free trial.

FAQ: Applying AI in Software Development

1. What is AI in software development?

AI in software development refers to the integration of artificial intelligence techniques and tools to enhance the software development process. It can be used for various purposes such as improving code quality, automating repetitive tasks, bug detection, and optimizing performance.

2. Why should I consider using AI in software development?

AI can significantly improve the efficiency of software development processes. By automating repetitive tasks, identifying bugs quickly, and optimizing code, AI can save time and resources, allowing developers to focus on more strategic aspects of their projects.

3. What are some best practices for applying AI in software development?

Some best practices for applying AI in software development include:

  • Start by identifying suitable use cases
  • Invest in quality training data
  • Ensure that your AI system is scalable and adaptable
  • Continuously monitor and fine-tune the system
  • Prioritize ethical considerations, such as fairness and transparency

4. Can you provide examples of AI applications in software development?

Examples of AI applications in software development include:

  • Code completion tools like GitHub's Copilot, which can generate code snippets based on natural language queries
  • Bug prediction and detection tools, such as DeepCode, which leverage AI to identify potential issues in code
  • Automated testing tools like Applitools, which use AI to carry out visual testing of software applications

5. What programming languages can AI be applied to?

AI can be applied to virtually any programming language, as long as there are sufficient tools and resources available for that language. Popular languages like Python, Java, JavaScript, and C++ often have more comprehensive AI libraries and tools.

6. How do I get started with AI in software development?

To get started, identify the specific areas in your software development process where AI can have the most significant impact. Then, research relevant AI tools and libraries, and invest in training data to build a customized solution. Keep in mind that AI integration may require updating your existing infrastructure and upskilling your team.

7. What are the potential drawbacks of using AI in software development?

Potential drawbacks include over-reliance on AI, which can lead to loss of human expertise, and the possibility of biased or unexplainable AI decisions. Additionally, integrating AI into your software development process may require a significant investment in resources, time, and training.