How To Manage Machine Learning Projects for Maximum ROI
To build a successful machine learning product, you need to understand how to manage a machine learning project. This takes a lot of soft skills, from product discovery to execution; there is a lot more to building a machine learning solution than just knowing some algorithms!
"Building successful machine learning (ML) products extends beyond algorithmic expertise; it requires comprehensive project management and vital soft skills. This talk presents a structured framework for navigating ML projects from product discovery to deployment and iteration.
We emphasize the importance of understanding customer problems, market size, and ML solution feasibility during product discovery. Early assessment of data quality and infrastructure is crucial, influencing project complexity and cost.
Projects should progress through stages—Minimum Viable Product (MVP), growth, and maturity—with distinct goals and investments. Prioritizing high-gain, low-complexity projects maximizes return on investment.
Solution architecture translates business requirements into technical specifications, considering factors like inference rates and latency. Strategic planning establishes long-term vision through success metrics, milestones, timelines, and resource allocation, while tactical planning ensures coordinated execution.
Data and training pipelines form the backbone of ML projects, requiring integration and automation for retraining and deployment. Serving pipelines deliver value to users, addressing security, scalability, and user experience.
We discuss the necessity of testing and monitoring in production, thorough documentation, and the iterative nature of ML projects. This holistic approach, honed through experience, equips practitioners to lead ML projects to success."