Comment gérer des clients ayant des attentes irréalistes en Machine Learning ?

To effectively understand and meet the client's goals in machine learning, it's crucial to start a comprehensive discussion about their objectives, expectations, and desired outcomes.Listening to the client's perspective allows for a nuanced comprehension of their specific needs. Asking targeted questions is essential to uncover the intricacies of their business challenges, existing processes, and potential areas for improvement. Building a foundation of understanding allows you to tailor your approach, whether it involves developing predictive models, enhancing data analytics, or implementing automation. It also facilitates the establishment of realistic timelines and milestones, fostering transparency and alignment throughout the project.

Be a teacher to your business partner and help demystify the machine learning, data science and analytical process. The best way to do this is to really get into the business problem and objective your client is looking to achieve. Consider a series of scope alignment workshops to understand the business problem, the constraints for deploying a solution, and business architecture where a potential solution will live. In many cases, what is possible at a theoretical level may not be possible to implement at a business architecture level. So to avoid wasted time/effort, it's best to define scope, identify constraints, and set realistic expectations over what can be delivered to avoid scope creep and inflated expectations.

This is absolutely critical. Remember the client is looking to solve a business problem. A model with 99.999% accuracy won't be of any use if it does not solve the problem. At the same time, a model with 80% recall may leave the client ecstatic. So understand the client's needs as well as the technical feasibility for meeting those needs. In machine learning projects, a quick proof of concept model is perhaps the best way to get the client and the ML team on the same page. Fail early if you must.

Educating stakeholders about the nuances of machine learning isn't just about managing expectations; it's about fostering a realistic understanding. Machine learning isn't a crystal ball; it's a rigorous, evolving process. It involves collecting, analyzing, and interpreting data, but it's also about uncertainty. Results hinge on various factors—data quality, algorithm choice, and more. Explaining these complexities helps set a foundation of understanding. It's not about dampening excitement but encouraging hopeful understanding based on knowledge. When stakeholders grasp this, they're better equipped to collaborate effectively, appreciating the depth of the process and adjusting expectations with a clearer lens, for project success.

Machine learning is an iterative process that involves collecting data, preprocessing the data, training the model, and evaluating the results. The iterative nature of the machine learning process means that iterations are necessary to refine models. Challenges include data quality problems, which necessitate extensive cleaning and require large, representative data sets. The complexity of the model may result in overfitting, and regularization techniques to correct the model. Hyperparameter tuning (Htuning) is essential to achieve optimal performance. Fluctuations, such as model inaccuracies, are natural but can be alleviated by continuous refinement. Emphasize the importance of continuous monitoring and adapting to changing data patterns.

You'll want to overcome what clients percieve to be "data science magic." There is an inclination to become overly optimistic about what machine learning can do. In some ways, training a model and teaching a machine to identify patterns and predict outcomes is magical. But, there is a methodical process behind every machine learning model and project. In some cases a team of data scientists may have worked endlessly for months on a solution before they moved to implementation. For a client, the largest challenge will be to determine how accurate a model will need to be, to be considered useful and valuable to the business objectives. You client will care less about the process and more about the results and accuracy.

Communication is key in any machine learning project. Regular updates, in the form of meetings or reports, let clients know what’s happening, what’s working well, and what needs to be changed. Not only does this build trust, but it also ensures that clients understand that machine learning is iterative and dynamic. Transparency about failures or changes creates a collaborative environment, aligns expectations, and allows clients to make better decisions. Continuous engagement allows clients to share valuable insights and builds a relationship based on mutual understanding and shared success.

According to industry experts, more than 85% of machine learning projects fail. Why? We don't always know but one factor may be managing the scope and expectations of the project that deem it a success. Did the project set out to do what was promised? You want to see successful outcome when managing scope, so it's best to clearly define scope in writing, involve stakeholders early, and set realistic expectations. One thing I like to do is use agile methods and have a regular cadence for status updates to keep business partners in the loop. A client should not have to guess where a project is at in the machine learning process, but they should know the process is iterative and projects plans need regular review and updates on a weekly basis.

Machine learning projects generally raise high hopes, mostly due to it's hype in the market. This can bring the scope to unrealistic level, which needs to be managed through communication as frequently as possible. - Handle scope through frequent communication - Before committing, take few days to do a quick MVP to see possibilities and potential problems

Help the client understand the final model. Be sure to include lots of explainability in the end product. A well understood model also means that the client will know where the limitations are. If the client is able to tell how the model reached the decision, it is far easier to sell the concept further downstream, to other management and stakeholders, for example. A hands-on client will also be able to figure out if something is starting to go 'off' and this can result in earlier and cheaper correction - think data drift, for instance.

Thee will always be clients with unrealistic expectations who refuse to be educated. Me: This model will have an error of 0.5 units. Client: Okay, but the predictions will be correct all the time, right? Me: No, I'm sorry, that is not possible. Client: But XYZ company said so. Me: Alright then, I guess XYZ company gave it to you in writing. Client: Not yet, but they will. Me: sighs... In these situations, its probably best to save your own time and let the prospective client and XYZ company deal with their perfect solution.