Developing software that uses artificial intelligence (AI) can be surprising, and coding, testing, and making sense of the data requires a particular combination of expertise and skills. Furthermore, fine-tuning the system takes time, and AI-based software judgments can be difficult to explain at times.

My company focuses on creating software test automation tools that enable customers to create tests for a variety of platforms, including desktop PCs and mobile devices. We wanted to make writing and running these tests even easier, and avoid having to tailor the test for each platform.

Our study led to the adoption of natural language processing, which allows users of our program to define a test in plain English, as well as computer vision using optical character recognition to recognize things on a screen.

Here are some of the things we learned that you can use as you integrate AI into your products.

Make data an integral part of planning

An artificial neural network (ANN) is a layered structure of algorithms meant to generate intelligent judgments without the need for human interaction using data. We integrated an ANN into our system, fed it hundreds of thousands of data samples, and then sat back and watched it make smart decisions.

Planning is critical in a system that relies largely on data. We had to deal with:

  • What data do we need to train the model
  • How to acquire, clean, and classify that data
  • How to obtain additional data from customers

This necessitated broadening the product management team’s responsibility, which has typically focused on the product’s features and capabilities, to include oversight of the system’s data-related components. This included establishing the data’s scope, approval criteria, and how the data would be used within our AI models.

Lesson learned: Data must be at the forefront of everything your team does, and your product managers must become aware of the AI methodologies your team use in order to assure consistency and dependable results.

Decouple the AI model from your product

It can take a long time to develop and tune an AI model. If your application is strongly linked to the model, you can only progress at the same rate as the model.

The AI model should be separated from the rest of the system and handled as a pipeline in its own right. This allows each component of the system to develop at its own speed, and modifications to the AI model can be applied individually. This has two major advantages:

  • You can develop and test your main product independently of the model, giving you quick feedback on features unrelated to the AI portion of the product, and you can continue developing and training the AI model without being slowed down by unrelated issues, such as a code change to the main product that breaks the build and causes everyone to wait until it’s fixed.
  • Your core product and the AI model can be released at different times. This is especially important for customers of our on-premises product because they can install it once and then update the AI model without having to go through a lengthy upgrade procedure. Given that AI models are designed to learn, adapt, and improve over time, this is a critical capability that allows our users to stay on the cutting edge of AI without having to wait for product upgrades.

Designing the system in such a way that the AI model can be built and deployed independently is a critical capability that you should address as soon as possible. Our release schedule now includes two timelines: one for the product and another for AI model improvements.

Create cross-functional, multi-disciplinary teams

Our teams were able to design and test the AI model separately after decoupling it from the primary product. However, we also wanted to test the system as a whole, with all of the components installed and functioning properly. End-to-end testing necessitates knowledge of both AI and software testing.

Software engineers, data scientists, data analysts, testers, architects, and the product manager formed cross-functional teams. This provided us with the best of both worlds since we now had professionals in AI model creation and development working alongside our software engineers and testers. We can use the complete team’s knowledge and experience to build, test, and deliver each component separately while also testing the entire system holistically.

This strategy has aided in the cross-pollination of specialized knowledge throughout the organization, allowing our developers and testers to gain a better understanding of AI while our AI experts learn to become better developers and testers.

Understand that explaining results in an AI system can be challenging

We like to conceive of our deep learning system as a black box that knows how to think and make judgments, yet it occasionally makes unexpected choices. You can troubleshoot a typical software system when it performs something unexpected. It’ll take some time, but you’ll figure it out eventually. However, in an AI system, determining the combinations and sequences of facts and reasoning that lead to a decision is nearly impossible.

The most efficient way to impact a model’s decisions is through supervised trial and error, combined with advice from AI experts who know how the model works and can direct the learning and tuning process toward more accurate results.

Expect longer cycle times when building the product

Traditional software products compile quickly—even major enterprise software products take only a few hours to produce.

AI models differ from one another. Gathering data samples, cleaning and labeling the data, and then training a neural network can take days, depending on the quantity and quality of the data you need. After that, you can begin the training process, which can take several days depending on the training cycle. In our situation, training just one model takes roughly three days on a system with a strong processor.

As previously noted, this is a primary rationale for separating the AI model from the rest of the product, decreasing dependencies, and creating a separate and independent pipeline.

Retrain your AI models with customer data

In an autonomous, continuously evolving, self-learning system, 100 percent accuracy, and zero flaws are impossible to accomplish. We spend a lot of time training our AI models in our own labs, but when they’re put in front of clients, they have to make judgments about things they’ve never seen before. The most effective way to fine-tune the system so that it makes the best decisions is to add consumer data to the model’s training data.

By getting their permission to utilize their data to retrain and enhance our models, we collaborate with our clients to increase the accuracy of our systems in their surroundings. This aids the model in making better judgments, resulting in greater results for our consumers.

Apply these strategies to your own AI development

The software business is witnessing an artificial intelligence revolution, with suppliers introducing new AI features to their products on a daily basis. My company has made big changes to how we build and deploy software, and we’ve restructured our teams to include AI experts, which you’ll have to do as well. We also collaborate with our clients more closely than ever before to learn from their experiences and enhance their outcomes.

AI development is difficult, but it is worthwhile. If you’re a part of the AI revolution, make sure you incorporate these tactics into your software development process to get the most out of it for your team, product, and users.

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