Both business and Engineering should go hand in hand. Business occurs due to the engineering breakthroughs and technology used. Engineering is made possible due to the funds driven by business. We can't say one is more important than other. We need to have separate teams for both business and engineering with complete focus on their own fields. Some business aspects should be applied to engineering like making the model cost efficient, fast, optimized for bandwidth and gpu costs. Some engineering aspects should be taken care while running the business like capturing the data of customers. Blend of both the parties results in success!

Bridging the gap between engineering and business in machine learning (ML) projects can indeed be challenging. Here are some strategies to help: 1.Foster Communication and Collaboration: Encourage regular communication between business leaders and technical teams. This helps ensure that both sides understand each other’s goals and constraints 2.Democratize ML Tools: Provide user-friendly ML tools to non-technical teams and robust development platforms to engineers. This can help both sides work more effectively and collaboratively 3.Focus on Real-World Applications: Engage in ML projects that solve real business problems. 4.Implement MLOps Practices 5.Continuous Learning and Training

Focus on business impact. Most ML projects fail because they optimize for publicity and not business impact. Your project needs to solve a problem, solving that problem must create value for your business (more revenue, less cost, higher productivity), and there exists a way to measure success quantitatively. When making design choices in model development, always be aware of the tradeoffs between model complexity and business payoffs.

Bridging the Engineering-Business Gap in ML Projects 1. Establish a Shared Language: Create clear documentation that simplifies technical concepts for business professionals, fostering mutual understanding. 2. Foster Cross-Disciplinary Teams: Encourage collaboration between engineers and business stakeholders to promote ongoing dialogue and problem-solving. 3. Utilize Project Management Tools: Implement tools that provide visibility into development progress while highlighting the business impact of ML initiatives. 4. Set Clear Objectives: Align project goals with business objectives to ensure engineering efforts contribute to organizational success.

One thing I found helpful is bridging the communication gap between engineering and business teams in ML projects (before the engineers strutter into build something that predicts everything except what the business actually needs). Engineers love complex models, while business teams care about practical results. The key is to align early: translate business goals into ML objectives that make sense to both sides. Regular check-ins and cross-functional collaboration can also ensure that both teams stay focused on the desired outcome, making the project both technically sound and valuable to the business.