AI Software Development Best Practices for Scalability and Performance

AI has seen immense growth in recent years, and AI software development practices have become increasingly ingenious. New models appear at an impressive pace, leading to the emergence of more and more interesting AI solutions. However, AI-powered apps often have a rather complex architecture. This tends to incur issues with app scalability and performance. Fortunately, there are tried and trusted AI development best practices for building scalable and high-performing apps. Keep reading to get valuable insights on keeping your AI app scalability and performance high. 

The value of scalability for AI software development

Scalability is a critical factor in AI software development due to the increasing complexity and volume of data. Besides, many companies need to adjust their software solutions to business growth. These are the main reasons making the companies focus on AI development scalability:  

• Handling large volumes of data. AI applications often require processing vast amounts of data. This data is vital for training machine learning models, performing inference, and generating insights. Scalability enables AI systems to handle large volumes of data efficiently, without compromising performance or reliability. By scaling both horizontally and vertically, organizations can adjust their software to growing data volumes. This factor is critical for keeping AI systems responsive and effective.
• Supporting complex AI models. AI algorithms and models are becoming increasingly complex. Sometimes, such models require massive computing power and memory to run their workflows. Scalability enables organizations to scale out computation and memory resources to support large-scale AI models effectively. That’s why scalable infrastructures become vital in AI software development. 
• Adapting to changing requirements. AI workloads and requirements can vary significantly. Much depends on factors such as user demand, data availability, and application context. Scalability allows businesses to quickly scale their AI systems up or down in response to changing workloads and requirements. In this case, automation for all scale-ups and scale-downs becomes important. It allows companies to save time and manage their systems more effectively. 
• Enhancing innovation and experiments. Scalability enables organizations to experiment and innovate with AI technologies at scale. Scalable solutions are not limited by infrastructure constraints and bottlenecks. Scalable systems also allow businesses to enhance the development and iteration of AI models and applications. As a result, scalability becomes the backbone for innovation that can bring businesses a competitive advantage.
• Keeping the operations efficient and cost-effective. Scalability allows businesses to optimize the use of resources. As a result, they minimize operational costs by dynamically allocating resources based on demand. By scaling resources up or down as needed, organizations can avoid over-provisioning and under-utilization of infrastructure. These benefits translate into better system performance and lower management costs. 

The value of performance for AI software development

Apart from scalability, performance is also an essential factor behind the success of an AI app. It defines user experience and is critical for the overall application quality. These are the main reasons to value AI development performance: 

• App reliability. High performance ensures that AI applications deliver accurate and reliable results. High-performance AI models can effectively analyze data, identify patterns, and make predictions with a high degree of accuracy. This means that the app brings real value to the stakeholders and builds trust among the users. 
• Enhanced user experience. Good performance is vital for user experience. The point is that any user feels comfortable while dealing with a fast, responsive, and seamless AI application. Users expect AI-driven applications to deliver timely responses and insights, without noticeable delays or interruptions. A high-performing AI app that keeps user experience standards high has a great chance of market success. 
• Competitive advantage. Good performance provides a competitive advantage. It enables organizations to differentiate their AI applications in the market and outperform competitors. This allows businesses to gain a first-mover advantage, establish market leadership, and capture market share in rapidly evolving industries and domains.
• Cost savings and ROI. The performance of AI applications translates into cost savings and a higher return on investment (ROI) for organizations. The point is in resource and AI development optimization. Enhanced app reliability also reduces the costs associated with errors, rework, and manual intervention.
• Enhanced innovation. Good performance fosters innovation and drives business impact. Organizations can use high-performing AI technologies to solve complex problems, drive insights, and unlock new opportunities. Analyzing large volumes of data to drive strategic decision-making is no longer a challenge to such businesses. Besides, high-performing apps are excellent solutions for real-time operations that also drive business efficiency. 

What are AI development best practices for performance and scalability?

Now that we understand why performance and scalability are crucial in AI software development, let’s dive into some practical tips. These are the practices that will help you achieve such goals in your AI-driven app. AI development best practices cover everything from big-picture operational decisions to specific technical strategies. Surely, there is no universal solution to all AI software development challenges. However, the provided suggestions will help you focus on scalability and performance while developing an AI app. 

Define user scenarios and enlist functional and non-functional requirements

Before embarking on AI software development, it’s essential to run a discovery phase. It will help you understand the functional and non-functional requirements of your solution. While functional requirements focus on solution features, non-functional requirements will help you define parameters such as processed system loads, acceptable solution downtime, and response time for various features. Also, come up with user scenarios for your app. This will help you anticipate its technical demands. User scenarios will also be essential for defining your app’s business goals. By defining clear objectives, developers can focus on building user-centric AI solutions. This will help specialists build a more accurate roadmap, come up with detailed estimates, and make the best technical decisions. 

Gather high-quality data

Data is the foundation of AI software, and its quality directly impacts the performance and reliability of AI models. Invest time and resources in collecting, curating, and preprocessing high-quality data relevant to the problem domain. By avoiding poor and unnecessary data, you automatically solve many scalability issues. Also, if your solution relies on fresh and relevant data, it is much easier to keep its performance high. Be sure that the data is representative, diverse, and free from biases or inaccuracies that could adversely affect model performance. Data governance practices and constant monitoring of the data environment will be crucial in this case. 

Select appropriate algorithms and models

Choose algorithms and models that are well-suited to the problem domain and objectives of the AI system. Factors you should consider include scalability, interpretability, and computational efficiency when selecting algorithms. Leave your AI software developers enough flexibility. They should be able to experiment with different modeling techniques, such as deep learning, machine learning, and statistical methods. As a result, the team will be able to find the most efficient way to ensure great performance and app scalability. In some cases, the most innovative approaches fit best. Regularly evaluate different algorithms to ensure optimal performance model performance. If needed, your team should be ready to modify models and algorithms.

Design robust and flexible architectures

By building an architecture with scalability in mind, you can accommodate it to varying data volumes, computational resources, and deployment environments. To ensure greater flexibility, rely on modular and reusable design patterns. Be sure that your software integrates with external scripts and solutions. That’s why you should focus on building interoperability in your AI models. Also, run regular reviews of these models. Use techniques such as feature importance analysis, model visualization, and explainable AI (XAI) to provide insights into the decision-making process of AI systems. All model assumptions, limitations, and potential biases should be documented. This will be crucial for informed decision-making. 

Rely on scalable database technologies

Scalability is often associated with cloud technologies. By relying on the world’s best providers of cloud solutions, such as Azure, GCP, and AWS, you will ensure excellent app scalability. These cloud providers can easily scale your infrastructure up and down on demand. The cost of their services is also, mostly, moderate. If you need more control over your data infrastructure, you may go with your own database solution. Ideally, opt for a private cloud adjusted to your requirements. Scaling such solutions is much simpler than dealing with on-premises technologies. Remember that different use cases may require different database solutions. Conduct a thorough research of available options to make the best decision. In some cases, it’s best to connect with database management consulting experts who will help you make the best choices based on their expertise.  

Foster collaboration and interdisciplinary expertise

This practice can be applied to the level of company culture. The key point is to foster collaboration. Knowledge sharing among multidisciplinary teams with expertise in data science, software engineering, and domain knowledge will help your teams collaborate more efficiently. A shared depository of valuable information and a culture where each stakeholder can make an input will help you build more efficient AI models. This, in turn, will translate into better AI app performance. The problem is that such an approach can also lead your company to chaos. A chaotic data infrastructure can negatively affect the efficiency of your AI models, leading to even greater scalability and performance issues. That’s why you should establish monitoring and rule-based collaboration policies. When you stay in control of your culture of collaboration, you will leverage its many benefits. Input-drive AI software development may help you come up with the most innovative AI development best practices. 

Continuously monitor the performance and run regular tests

The value of monitoring in AI software development has already been mentioned. However, this question deserves more specific attention. You should monitor the performance and behavior of AI systems in real-world environments to identify performance degradation, drift, or unintended consequences. Don’t forget about the value of monitoring your data. Any data anomalies or errors can disrupt the work of your models, leading to performance issues. Implement monitoring and feedback loops to capture and analyze data on your solution’s performance. Continuously evaluate and iterate on AI models and algorithms to improve performance, adapt to changing requirements, and mitigate risks and uncertainties in AI software development. For this task, you should come up with sets of tests, either automated or manual. Unlike old-fashioned apps, AI software is very dynamic. That’s why you should run tests regularly. In this case, you will identify any issues or system changes long before they affect the end user and cause real scalability and performance issues. 

Conclusions

In conclusion, ensuring scalability and performance in AI software development is essential for business growth, user experience, operational efficiency, and many other reasons. Certain best practices help teams focus on these factors in AI software development. However, this question requires a holistic approach that integrates technical expertise, clear methodology, and accurate model supervision. By following these best practices, organizations can build robust, scalable, and trustworthy AI solutions.

Adhering to the practices mentioned above may be a challenging task. That’s why you need to rely on the most trustworthy specialists. Discover how we at nCube assisted Veritone in creating the first AI operating system in our case study. If you need a team of AI developers for your project, don’t hesitate to reach out to us.