To ease friction in ML model integration, establish a unified workflow using CI/CD pipelines and tools that meet both data science and engineering needs. Schedule regular, agenda-driven meetings and maintain shared documentation to clarify objectives and specifications. Implement structured feedback loops, such as post-deployment reviews, to promptly address issues and refine processes. Use real-time communication tools to track updates, and encourage cross-training for team understanding. Regularly assess and adapt workflows to ensure alignment, fostering a collaborative and transparent environment that supports efficient ML integration.

??Encourage open communication through regular syncs and shared documentation. ??Develop a unified workflow using CI/CD pipelines to streamline deployment. ??Use tools both teams are comfortable with to ensure smooth integration. ??Offer cross-training for team members to understand each other's challenges. ??Establish clear roles and responsibilities to minimize overlap and confusion. ??Involve both teams in planning and decision-making to foster collaboration. ??Continuously monitor and adapt processes to improve efficiency and teamwork.

??The IS–LM model also allows for the role of monetary policy. If the money supply is increased, that shifts the LM curve downward or to the right, lowering interest rates and raising equilibrium national income. ??The money supply increases, and the interest rate falls. The LM curve shift to the right when money supply increases. This is because when money supply increases, the interest rate is lower at each level of Y. Increase in money supply will shift the LM curve to the right from LM1 to LM2, a long the IS curve.The economy moves down along the IS curve: the fall in the interest rate raises investment demand, which has a multiplier effect on consumption.

1. Establish Clear Communication: Set regular meetings for cross-team updates, making sure data scientists, engineers, and product teams understand each other’s needs. 2. Define Roles and Responsibilities: Clarify who owns which part of the ML pipeline to prevent overlap and gaps in accountability. 3. Adopt a Unified Framework: Use tools and platforms that streamline model deployment and monitoring, like MLOps platforms, to align workflows. 4. Create Documentation: Maintain thorough documentation for model specifications, expectations, and dependencies to ensure transparency. 5. Encourage a Collaborative Culture: Promote knowledge-sharing sessions and cross-functional training to builf understanding and empathy across teams.

To bridge the gap in ML model integration, start by fostering open communication through regular meetings and shared documentation to keep teams aligned. Create a unified workflow by adopting tools familiar to both data science and engineering teams, like CI/CD pipelines. Finally, promote cross-training so team members can understand each other's roles, fostering empathy and collaboration. This approach encourages seamless model deployment, improving efficiency and team cohesion.