Project managers and data engineers clash on ML expectations. How can you align their visions for success?

From what I know and learn about Machine Learning, project managers steer projects forward, monitoring timelines and budgets while serving as liaisons between the technical team and stakeholders. Conversely, data engineers concentrate on the foundational technical aspects vital for ML initiatives, encompassing data processing, refining, and model deployment. Acknowledging these distinct roles allows for mutual respect and understanding of each other's value and constraints. By grasping the complementary nature of their responsibilities, project managers and data engineers can collaborate effectively, leverage their strengths, and collectively drive successful ML projects to fruition.

ML projects thrive on teamwork, but PMs and data engineers can clash due to different expectations. And, to avoid these understanding each other role is extremely critical. PMs: Focused on efficiency, timelines, deliverables. May struggle with ML's uncertainty. Data Engineers: Prioritize data quality, infrastructure, model foundation. May find PM timelines restrictive. And, to bridge gap further following approach can be taken :- 1. Open Communication 2. Data-Driven Decisions 3. Focus on Outcomes

Project manager role is different from data engineer role in many aspects. The former works towards ensuring the client expectations and product requirements are met whereas the later works towards building large scale data pipelines for ensuring a smooth data flow process.

As a data professional, if there are differences in expectation, try to figure out what is the root cause. Ask why they think the project will take X time. Thereafter, try giving a breakdown of all steps involved in your task. Be aware that task breakdowns might be seen as a foreign language to non-technical stakeholders and be ready to have an analogy to help bridge the gap. If the non-technical stakeholder does not agree with your timeline, you can explain the ramifications from removing a task from the pipeline. Always link the value of the task back to the business.

I've navigated the complex landscape of aligning project managers and data engineers in ML projects. Trust me, communication is the linchpin. Regular sync-ups are crucial. Project managers need to grasp the tech hurdles—ML isn't just plug-and-play. Simultaneously, data engineers should tune into the bigger picture and timelines. When both sides understand each other's world, it becomes easier to move toward a unified goal. It's all about creating a dialogue where challenges, expectations, and progress are transparently shared. Consistent updates keep everyone aligned and drive the project toward success.

Try to align the objectives based on a resources/performance ratio. For each element, present different possibility (with x time, we can have y accuracy, with x gpu we can have y parameters...)

Machine learning is nothing but research into the mind of a human by decoding it from a data engineer perspective. This process needs for human effort than timelines and deliverables for building a strong algorithm which serves the business logic. Yet, project managers play a pivotal role in dealing with business owners and stakeholders regarding deliverables and timely conversion of research to their business. Engineers need to communicate all challenges and technical hurdles without delay. Clarity would enable a project manager to act on time. Both sides need to be flexible in mindset to cater to changing project needs.

When managing ML projects, I've found that flexibility is crucial. Project managers often have tight schedules, but we need to understand that model tuning and data wrangling can throw a wrench in even the best-laid plans. One thing I've learned from my experience is the importance of open communication. Data engineers should feel comfortable voicing technical hurdles as they arise, and project managers must be prepared to pivot as needed. This can be a game-changer in navigating the unpredictable nature of ML projects. Being adaptable isn't just a nice-to-have; it's a must-have for harmony and success.

Staying on top of ML advancements isn't just a bonus; it's essential. As a project manager, I've seen how understanding ML trends can set realistic goals and avoid overruns. Data engineers, on the other hand, make projects hum by adopting the latest in data processing and model development. Continuous learning keeps both sides speaking the same language, reducing friction and aligning their visions. By committing to learning, you're not just keeping pace—you're setting yourself up for success and ensuring project cohesion. It's all about staying curious and open to new methodologies and tools. Embrace it, and watch your projects thrive.

Develop a detailed project roadmap that outlines each phase of the ML project, from data collection and preprocessing to model deployment and maintenance. This roadmap should include timelines, milestones, and deliverables that both sides agree upon.