To manage unrealistic ML expectations, start with clear communication about actual capabilities and limitations. Present case studies showing realistic outcomes from similar projects. Create proof-of-concept demonstrations to show achievable results. Document constraints and trade-offs transparently. Implement phased delivery to show incremental value. By combining honest assessment with practical evidence, you can align expectations with realistic ML possibilities while maintaining stakeholder confidence.

To manage unrealistic ML expectations, I prioritize clear communication, educating stakeholders on ML's capabilities and limitations, and setting realistic, achievable goals with well-defined metrics. Regular progress updates and transparent discussions about challenges are crucial to maintain alignment and adjust expectations as needed. This approach fosters a collaborative environment where everyone understands the ML journey and its potential while acknowledging its inherent limitations.

To manage expectations, clearly communicate the limitations of machine learning, emphasizing data quality, model complexity, and required time for development. Set realistic milestones and highlight the iterative nature of machine learning. Provide examples of achievable outcomes to align expectations with practical results.

When faced with unrealistic machine learning expectations from my business team, I prioritize clear communication and education. I explain ML's capabilities and limitations in straightforward terms, set achievable goals aligned with our current resources, and provide regular progress updates to maintain alignment. This approach ensures that all stakeholders have a realistic understanding of what can be accomplished, fostering a collaborative environment where expectations are managed effectively.

Explain Data Limitations: - To get to the level of their expectation, what does the structure/quality of the data need to look like and where is it currently? Garbage in = Garbage Out. Is it realistic? - Stakeholders typically like examples. Showcase what the current ML result yields and what features/resources would be needed to execute a more accurate output that meets their expectations. - Depending on the technical ability of the stakeholder, I usually avoid using deeper ML terminology as it convolutes any sort of meaningful conversation. Leverage their ambitious expectation with the reality of resources/data at hand and drive towards smaller goals first.

When dealing with unrealistic expectations in machine learning projects, I focus on three key strategies: 1. Educating stakeholders: I make sure non-technical team members understand ML’s capabilities and limitations through clear, simple explanations. 2. Setting realistic goals: I align milestones and performance metrics with the current state of data and technology to ensure feasibility. 3. Transparent communication: Regular updates on progress, challenges, and necessary adjustments keep everyone informed and aligned. This approach fosters trust, clarity, and sustainable project outcomes.

Keep it short and simple : Clarify Feasibility: Explain what is technically possible and highlight constraints like data quality, computational resources, or model limitations. Set Measurable Goals: Define realistic KPIs (e.g., accuracy, speed) based on current capabilities/ Showcase Trade-offs: Illustrate the impact of pushing beyond limits, such as increased costs, longer timelines, or reduced reliability. Don't be delusional, be realistic. Educate with Examples: Use case studies or benchmarks to show typical ML outcomes, helping them understand industry norms.

As program manager for the Air Force: Weapons engagement OPtimizeR (WOPR), I've had to fight off tons of unrealistic goal setting. I liken WOPR to the field of insect intelligence. No ant is a genius, but together, they accomplish great things. Most AI and ML are currently specialized and fit within a much bigger picture powered by large human teams.

This is something that I have myself faced in my career and to tackled these unrealistic demands, we can think about the following methods. Educate: Explain ML capabilities, limitations, and data dependencies clearly. Set Realistic Goals: Align objectives with achievable outcomes through early discussions. Transparent Communication: Share model performance metrics and trade-offs regularly. Proof of Concept: Demonstrate results on a small scale to validate feasibility. Iterative Process: Emphasize that ML models improve over time with feedback and data. Collaborate: Involve stakeholders in refining goals and understanding constraints.

Using real-world examples of successful ML applications—along with their limitations—helps stakeholders better understand what’s achievable. Starting with a proof of concept or pilot project also allows the team to see tangible results and refine their expectations. Transparent communication throughout the process is key to maintaining trust and alignment.