Create a strong risk management plan that focuses on early identification and solving problems. Use repeated development cycles to check assumptions and adjust quickly to unexpected challenges. Involve teams from different areas to get various viewpoints and encourage a culture of open communication where concerns are addressed early. Use real time monitoring tools to keep track of project milestones and performance, allowing you to make changes as needed. By combining a flexible approach with ongoing learning, you can turn risks into useful information, ultimately guiding your project to successful results while keeping up the pace and clarity.

To manage risks and ensure your ML project stays on track: 1. Identify risks early and prioritize the biggest ones. 2. Create backup plans for key risks, like alternative data or models. 3. Set clear milestones to monitor progress and spot issues early. 4. Communicate frequently with stakeholders to manage expectations. 5. Test continuously to catch problems before they escalate. This keeps the project aligned with its timeline and goals.

Break down the project into smaller, manageable chunks with clear milestones. This helps you track progress and catch any delays early. Regularly assessing these milestones keeps everyone aligned and makes it easier to adjust timelines if something unexpected comes up. Plus, it gives a sense of accomplishment as you hit each target.