In today's reality, clients want to solve their business problems with data/AI solutions. Often businesses will already have an idea of the solution they want, which is not necessarily what they need or what is going to create a real impact. As data scientists, our job is to work closely with clients to define their problem and build tech solutions to solve it, delivering value for their business.

But how do we design and build tech solutions that are scalable, maintainable, often in a short timeframe, that will not fall over the first sign of a new technological update? How do we design solutions in a way that fosters creative and innovative thinking, while also providing strong foundations in an ever-evolving landscape?

Join me to discuss a practical and opinionated checklist to follow when connecting business needs with technical solutions, highlighting key considerations when designing the architecture and code behind them. With a real business example, I will walk through the important steps to ensure we are bridging the gap between business problems and real-world solutions, highlighting the benefits for business and development teams alike.