What do you do if your short-term objectives clash with your long-term strategic vision in Machine Learning?

This question is not phrased correctly in order to elicit useful answers. First of all, Short-Term objectives need to be classified into critical (that might result in the disintegration of the system) and distractions (that might feel like success in the short term but will sap your bandwidth). When viewed in this light, one can clearly see what should be done hence making this question redundant.

Long term vision only matters when you are presently succeeding at making progress. If you haven’t already found success in approaching you long term vision, then first focus on the shortest term vision you can have. For example, if you are going for an industrial long term vision and you haven’t made money yet from it, then take a breather and reset so you can first find something that makes money. I spent a long time building a few computer vision solutions and I quickly found that going for the fastest to market option was the best option. In tech, it’s very easy to target a long term uncertain vision and stumble into a game of funding, marketing and pre-product sales, instead of actually making something useful now. Be useful now ??

Assessing reality in machine learning helps manage immediate goals, which can clash with long-term plans. For e.g., quick fixes based on current data may not align with future objectives. It's like patching a leak fast to prevent damage, knowing a full renovation is needed later.

When short-term objectives clash with long-term strategic vision in ML, it's crucial to prioritize goals effectively. Evaluate the significance and impact of each objective in relation to the overarching strategic vision. Consider factors such as alignment with organizational goals, resource allocation, and potential trade-offs. Prioritize objectives that align most closely with the long-term vision, while ensuring that short-term goals contribute to incremental progress towards that vision. Communicate transparently with stakeholders about the rationale behind prioritization decisions and seek alignment where possible. By prioritizing goals strategically, you can navigate conflicts and steer your ML initiatives towards long-term success.

Prioritizing goals in machine learning ensures efficient resource use and alignment with key objectives. To prioritize, define measurable goals, assess their impact and urgency, and employ methods like the Eisenhower Matrix, MoSCoW method, or SMART criteria. These methods help categorize and rank goals, balancing immediate and long-term needs.

Strategic flexibility in machine learning lets you quickly adapt to changes, ensuring your work stays relevant and impactful. It helps balance immediate tasks with long-term goals, keeping your overall vision on track.

Integrated learning in machine learning involves combining knowledge from various sources and experiences, enhancing the system's ability to adapt and make informed decisions. This approach can improve strategic flexibility by providing a broader perspective and deeper insights, enabling more effective prioritization and alignment of short-term actions with long-term objectives.

Clear communication ensures everyone understands the goals, priorities, and strategies, facilitating better collaboration and decision-making. This alignment helps effectively balance short-term tasks with the long-term vision in machine learning projects.