¿Qué hacer si su equipo de expertos en aprendizaje automático no cumple con las expectativas?

There are several steps you can take to address the issue: 1. Clear Communication: Ensure expectations are clearly understood. 2. Identify the Problem: Understand the root cause - skill gap, obstacles, or motivation issues. 3. Provide Resources: Offer training or resources if they lack skills or knowledge. 4. Review Processes: Check if workflow, project management, or communication processes need improvement. 5. Performance Feedback: Regularly provide constructive feedback for improvement. 6. Motivation and Team Building: Boost morale and foster a positive work environment. 7. Reevaluate Team Structure: Consider role changes or new team members if performance doesn't improve.

ML engineers come in a variety of flavours and there is no "one-engineer, fits-all" strategy that works all the time. Organizations should be more proactive than reactive in matching their ML hires to their business requirements, and provide up-skilling opportunities where knowledge gaps occur. Avoid "hard-talks" at all costs as it's a No. 1 buzz-killer.

If your team of machine learning experts isn't meeting expectations, it's crucial to review processes to identify areas for improvement. Start by analyzing the workflow, from data collection and preprocessing to model development and evaluation. Consider whether there are bottlenecks or inefficiencies in the pipeline that could be hindering progress. Encourage open communication within the team to address any challenges or roadblocks they may be facing. Additionally, provide opportunities for further training or upskilling to enhance their expertise. By fostering a supportive environment and implementing strategic improvements, you can empower your team to overcome obstacles and achieve success in their machine learning endeavors.

If you built the team and the team is not delivering, then consider that the problem may be as much with you as with the team. Have you been communicating clear expectations to them? When you hired them, did you ensure they would have the skills necessary to perform the roles you planned for them? Have you set them up for success in terms of collaboration with other stakeholders and access to the resources they depend on? In general, if you have one team member who isn't performing, maybe it's that one person. But if your team is not delivering as a whole, you are most likely the root cause. So you might do well to proceed from that assumption as you try to diagnose and address what is wrong with your team.

Successful teams are those who understand the customer requirements and convert them to technical problems that can be solved. It is always essential to have checkpoints to understand the work that we do is adding value to the customers. We should always understand the gap between the customer expectations and technical limitations. Constant feedback is very important to realise what is going wrong at the initial stages. A machine learning model will have no value if it does not add value to customer experience and as a technology user we should always have clarity on what a model can do to improve customer experience and the challenges come with ML models like continuous improvement and retraining , monitoring and data drift etc.