What are the best strategies for setting realistic expectations in Machine Learning?

Defining the problem involves a comprehensive understanding of the problem domain, including its nuances, challenges, and potential pitfalls. Clearly articulating the problem helps in managing stakeholders' expectations and avoiding overambitious goals that may lead to disappointment. Setting realistic expectations in machine learning begins with a well-defined problem statement. This involves comprehensive problem analysis, clear communication of objectives, constraints, and metrics, and a realistic assessment of available data. By establishing a solid foundation from the start, the project is better positioned for success, and stakeholders are more likely to have a clear understanding of what to expect.

In my experience, the best way to manage expectations through the model development and deployment pipelines is by gaining an wholistic understanding of the problem/question the ML model is been built to solve. The allows the engineers and developers to set clear milestones leading to unequivocal outcomes.

Another clever way to manage expectations is shrewd benchmarking. Running model performance against statutory benchmark e.g MMLU, SuperGlue, commonsenseQA improves the model generalization on novel datasets

The selection process should be driven by a careful consideration of the problem at hand, the available data, and the specific goals of the project. It's essential to match the complexity of the machine learning model with the complexity of the problem; using overly complex models for simple tasks can lead to overfitting and unrealistic performance expectations. Conversely, employing overly simplistic models for complex problems may result in underfitting. By thoughtfully selecting methods and tools that align with the nature of the problem, the available data, and the project goals, practitioners can establish a foundation for realistic expectations and enhance the overall success of the machine learning endeavor.

The 'No Free Lunch' theorem in machine learning underscores the importance of impartiality in choosing models or architectures. While the inherent characteristics of data can guide model selection, it's often more effective to evaluate several models through established metrics. This approach generally outperforms the selection of a model based solely on its current popularity or trendiness.

You need not only keep hammering the conventional stuff and using off the shelf models, but using statistical insights in data engineering, its impact on future deployment, and research on novel methodologies is also an indispensable armour of a data scientist. Everyday, every instance you will come across a unique situation and you need to be well equipped to confront the inevitable challenges.

Machine learning is not a linear or deterministic process, it is an iterative and experimental process that involves trial and error, feedback, and refinement. You need to set realistic and flexible milestones that reflect the complexity and the uncertainty of machine learning, and that allow you to monitor and adjust your progress and your expectations. You need to define the scope, the deliverables, the timeline, and the budget of each milestone, and communicate them clearly and transparently to your team, your stakeholders, and your users. You also need to be prepared for unexpected outcomes, failures, and changes, and learn from them.

Machine learning is not a black box, it is a transparent and accountable system that produces results that can be explained, verified, and validated. You need to evaluate and validate the results of your machine learning solution, using appropriate metrics, methods, and tools, and compare them with your expectations and your criteria of success. You need to analyze the performance, the accuracy, the robustness, and the reliability of your solution, and identify the sources of error, bias, and uncertainty. You also need to test and validate your solution with real users, in real scenarios, and collect feedback and suggestions for improvement.

Machine learning thrives on collaboration and interaction, involving team members, stakeholders, users, and customers. Effective communication is key in managing expectations, encompassing clear discussions about the goals, capabilities, limitations, and outcomes of your machine learning solution. It's essential to educate and inform others about the fundamental aspects and benefits of machine learning, along with its challenges and risks. Equally important is attentively listening to and addressing the needs, concerns, and feedback of all involved parties, thereby fostering trust and confidence in your solution.