Overview

Businesses are rapidly adopting Machine Learning, but successful integration requires more than just advanced technology—it demands effective project management. Understanding how ML fits into software and hardware solutions is essential for ensuring meaningful outcomes. This course covers the fundamentals of managing ML-driven projects, including how a Project Manager interacts with Machine Learning tools, integrates data analysis, and evaluates results for both internal performance and customer impact. You will explore what makes ML outcomes successful, the role of internal and external metrics, and strategies for incorporating ML into project workflows. This course is tailored for professionals who lead, support, or contribute to machine learning projects within technical environments. It’s ideal for technical project managers, team leads, and supervisors responsible for integrating ML tools into product development workflows. Developers aiming to understand how ML impacts future tasks and project delivery will also benefit, as will data analysts looking to better build and assess ML models. Additionally, this course is a strong fit for professionals who need to effectively communicate ML objectives and results to both technical teams and business stakeholders. To succeed in this course, learners should have a working knowledge of project management principles—such as Agile, Scrum, or similar methodologies—and a basic understanding of machine learning tools and concepts. Familiarity with large language models (LLMs), analytic workflows, and data processing techniques will help learners follow along more effectively. While coding experience is not required, comfort with data-driven thinking and some exposure to tools like R or Jupyter Notebooks will be advantageous. By the end of the course, learners will be able to manage ML-driven projects by implementing data analysis tools and aligning outcomes with defined performance and customer impact metrics. They will develop structured frameworks to integrate ML into project planning, enabling informed decision-making and strategic workflow enhancements. Learners will also identify critical success factors and formulate key evaluation questions to guide continuous improvement. Finally, they will assess ML project effectiveness using both internal benchmarks and external KPIs, ensuring alignment with organizational goals and stakeholder expectations.

Syllabus

Data and Machine Learning for Technical Product Managers

In this course, you’ll explore the core principles of managing machine learning (ML)-driven projects, focusing on how Project Managers engage with ML tools, integrate data analysis into decision-making, and assess outcomes to enhance both operational performance and customer value. Through practical insights, you’ll learn to define success in ML initiatives using internal and external metrics, and apply strategies for effectively embedding ML into existing project workflows. You’ll also examine key factors that influence ML effectiveness, developing a framework for sustainable and impactful AI integration in project environments.