Machine Learning Project Management
Machine Learning System Design
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Machine Learning Project Management
Machine learning is all about developing models? Think again! Machine learning is risky and costly, and we cannot get started on a project without planning, coordination, and collaboration. Here is my typical template for leading a machine learning project to success. Here are the critical steps, but each project may need some adjustment depending on the specificity of the project.
Product Discovery: What is the size of your market, and for what ML solution?
Like any business endeavor, building new software requires a deep understanding of your customer's problems and how likely those problems are to be addressed by a machine learning solution. This understanding of the problem is critical to estimating the potential monetary gain that such a new product or feature is likely to generate if implemented. It is also important to ideate if there is a feasible ML method that is likely to solve the established problem. This step is typically done as a collaboration between a technical lead and a product manager.
Assess the data infrastructure: This step will really define the project's complexity
It is critical to assess the quality of the data and the data infrastructure as soon as possible. This step will heavily influence the cost of the project or even its feasibility. The faster this assessment is done, the faster engineering work can be planned accordingly. This step is typically performed as a collaboration between a technical lead and the data engineering team.
Work in stages: Reduce the risk
Machine Learning projects can easily be broken into 3 stages:
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