Best Practices for Developing Scalable AI Solutions

As today’s marketplace continues to accelerate at a pace we have never seen before, scalable AI solutions are a requirement, not a luxury. With the use of artificial intelligence (AI), a startup, small business, or large enterprise can achieve increased productivity, better engagement with customers, and sustainable growth. This article provides best practices for constructing scalable AI solutions to facilitate digital transformation through the use of leading-edge AI tools, AI software, and machine learning technologies.

Define Clear Business Objectives

It is important to determine what specific problem you are solving before delving into development. Whether you want AI for lead generation, business automation, or customer support via an AI chatbot, a clearly defined use case makes it easier to decide which aspects of development are more relevant to attaining your business goals. For small businesses, this means focusing more on a solution with proper ROI with as little of an investment as possible.

Choose the Right AI Tools and Frameworks

The scalability of any AI solution relies heavily on the tools and platforms that were used to build it. Machine learning software and productivity tools are examples of popular AI software, with TensorFlow, PyTorch, and Google Cloud AI being three widely used solutions that enable developers to create intelligent applications. Each of these software solutions allows the integration of machine learning models into existing business processes. When developing a solution, it’s important to choose tools that are third-party, well-documented, and are being actively used and developed by the AI community.

Start Small, Then Scale

“Scalable AI” doesn’t mean starting big. For example, use a minimum viable product (MVP) to test the model’s accuracy, see whether it will work for your business, and understand if people can actually use it. This type of testing is particularly useful when implementing AI for small businesses, as there are often limited budgets and technical migration resources. And once the MVP is proven to work, the development can evolve and scale to consume data or multiple use cases.

Prioritize Data Quality and Management

Any AI solution relies heavily on data. The performance of our machine learning models is dependent on the quality, relevance, and volume of data they are based on. Following the establishment of robust data governance frameworks, compliance with privacy legislations, and performing sensible levels of routine data cleaning and preprocessing, it is important to make use of AI-run productivity tools to help automate data labelling and classification so that model training becomes increasingly frightfully efficient.