Your client insists on a more complex ML solution. How can you align their expectations with practicality?

Using more complex or advanced architectures and solutions for basic tasks and easily achieved by simpler methods can hold many cons; for example using deep learning for a classification task over simply interpretable data could easily make the model overfit and therefore perform poorly on production real use-cases, on top of a higher cost. Having a seasoned ML engineer is important in this case to make sure to choose the most appropriate solution to our problem and optimize performance and cost, as well as having a sustainable and robust solution.

When a client insists on a more complex machine learning (ML) solution, balancing their ambitious vision with practical execution is key. Here's how you can align their expectations effectively: Educate on feasibility: Explain the technical limitations and realistic outcomes of overly complex solutions. Showcase simpler alternatives: Present simplified solutions that meet core objectives without unnecessary complexity. Set clear timelines and milestones: Establish achievable goals to manage expectations and demonstrate progress.

This is how I handle client demands for complex machine-learning solutions: 1. Educate on Feasibility: Explain technical limitations and potential issues with complexity, like overfitting. 2. Showcase Simpler Alternatives: Present prototypes or case studies of effective, simpler solutions that achieve core objectives. 3. Set Clear Timelines and Milestones: Use an agile approach to establish achievable goals, ensuring regular updates and progress assessments. 4. Foster Open Communication: Maintain ongoing dialogue with the client to understand their vision while guiding them toward practical solutions.

Navigating a client's desire for a complex ML solution involves a blend of strategic alignment and practical execution: ? Highlight Maintenance: Emphasize the long-term maintenance and scalability challenges of overly complex solutions. ? Cost Implications: Discuss the potential cost implications, both upfront and ongoing, that come with added complexity. ? Risk Management: Point out the increased risk of failures and bugs in more intricate systems. ? Prototype First: Suggest starting with a prototype to validate the approach before diving into full-scale implementation. Balancing a client’s ambitious vision with practical execution helps ensure a smooth project trajectory.