To balance stakeholder expectations with practicality, I first thoroughly understand their requirements and analyze the most suitable solution for the use case—not necessarily the most complex one. By communicating this effectively, I ensure the solution is both practical and interpretable, aligning stakeholder expectations with achievable outcomes. Additionally, I involve stakeholders through iterative feedback loops, demonstrating prototypes early to align on the solution and adjust as needed.

When stakeholders demand complexity, it’s often because they equate it with innovation. But complexity isn’t always the hero—impact is. Start by aligning on the problem, not the solution. Show how simpler, scalable models can deliver value faster while keeping options open for future complexity. Use prototypes to prove that practicality doesn't compromise performance. Frame simplicity as agility: it’s easier to adapt and explain. In the end, stakeholders don’t want “complexity”; they want results—and a solution they can trust.

Balancing stakeholder expectations with practical solutions is always a challenge. As professionals, it’s our job to ensure those solutions are not just effective but also feasible. Here’s how we can handle this: 1. Start with the core problem: By understanding the real issue, we can design solutions that are focused and efficient. 2. Highlight the trade-offs: Complexity often comes with higher costs, longer timelines, and more Explaining this really helps. 3. Iterative approach: Instead of building something overly complicated right away, I prefer starting small and refining over time. 4.Use data to support decisions: Showing evidence of how simpler solutions can work just as well makes discussions more productive.

In my experience, stakeholders often lack a comprehensive understanding of the complexities involved in machine learning solutions, particularly concerning data, system, and technology requirements necessary to achieve desired business outcomes. This is especially true when such solutions are proposed merely to keep up with competitors. Therefore, it's crucial for data scientists to thoroughly educate stakeholders—sometimes tediously—about the pros and cons of the proposed solutions. This helps ensure their expectations are aligned with what is technically feasible.

All ML solutions should be evaluated based on their practicality, regardless of their complexity. It's essential to have an open conversation with stakeholders about the requirements for the solution. We need to consider how important this ML solution is for the business. Is it a temporary fix to a problem, or will it provide long-term benefits? It's also crucial to communicate the advantages and disadvantages of implementing the ML model, including any limitations and trade-offs involved. Setting realistic milestones after a decision is made and maintaining regular communication throughout the process ensures that everyone remains aligned.

I would balance stakeholder expectations by first understanding their goals and aligning them with what's feasible given the data, time, and resources. I’d start with a simple, proof-of-concept solution to demonstrate value quickly and set realistic expectations. Then, I’d work iteratively, improving the solution step-by-step while keeping stakeholders informed of progress, challenges and trade-offs. This approach ensures we deliver a practical, impactful solution without overpromising.👩💻⏳

Balancing stakeholder expectations with practicality in ML projects requires clear communication and alignment on goals. Begin by understanding the problem and determining if complexity truly adds value or if simpler solutions suffice. Transparently explain trade-offs, feasibility, and potential risks of complex models. Use prototypes or proofs of concept to showcase results early, shifting focus from complexity to effectiveness. Regular updates and feedback loops ensure alignment and help manage evolving expectations. Highlighting model interpretability and reliability often builds trust, steering stakeholders toward practical solutions that deliver real impact while staying aligned with their goals.

Start with a clear assessment of business requirements versus technical feasibility. Phase 1: Deliver MVP with core features Focus on highest-impact capabilities Establish baseline metrics Gather user feedback Phase 2: Iterative improvements Add complexity gradually Validate each enhancement Don't forget about regular stakeholder updates and a transparent timeline/roadmap. Good luck!

Data is crucial to the successful implementation of ML models. In healthcare projects I'm currently working on, it's important to have the right set of data and high-quality data. At times, it's very difficult to transform and unify the data as expected by ML models. While stakeholders may demand complex models, it's important to explain the limitations of data quality, model complexity, and resource constraints. Focusing on outcomes, following an iterative approach for developing ML models, and verifying the outcomes will ensure that the solution meets both technical feasibility and objectives.

Stakeholders expectation to put into realistic solution is really challenging task but ML engineer has to provide best possible ways to achieve realistic goals. Involving stakeholder in each stage of development leads to more accurate outcome.