Machine learning is all the rage today, and lots of people are interested in a machine learning career. When I get a chance to chat with people that “want to do machine learning stuff”, I usually ask what roles they’re interested in, and they usually mumble something like “Data Scientist”, in a very unconvincing way. 🙂
The good news is that in addition to data scientists, lots of other roles contribute to the process of building AI products. To highlight these roles, let’s walk through the process for building an AI product, and see who we need along the way.
According to Yael Gavin, a former Facebook PM (who by the way has a great series of posts on machine learning for PMs), the process for building an AI product (or ML model) is as follows:
1. Ideation: This is when the product team comes together to align on their goals, customer problems they want to solve in order to achieve those goals, and metrics to measure success. While this stage is primarily driven by the product manager, engineering lead, and UX designer, additional contributors include the product analyst, who helps the team to think deeply about metrics, and the data scientist who helps the team understand how machine learning capabilities can help solve the customer’s problem.
2. Data Prep: Here the product team decides how to collect the data necessary for training their ML models, and computing the metrics needed to measure their success. This stage is driven by engineering, with help from the product analyst, who makes sure that relevant metrics can be calculated, and the data scientist, who makes sure that required ML model inputs (also known as features) can be produced from the data.
3. Prototype & Testing: This is the heart of the model building process. Here the data scientist trains the ML models and checks to make sure that the predictions or classifications from the trained models are within acceptable levels of accuracy. This highly iterative process is driven by the data scientist, with some help from the product analyst and engineering. The product analyst tracks model accuracy metrics, while engineering helps the data scientist understand the data, and delivers additional data where necessary.
4. Productization: At this stage the model is doing what it’s supposed to, and is ready for primetime. The product manager, UX designer and engineering lead the process of hosting the model in a scalable manner, and building the delightful user experience that leverages the machine learning model to actually solve the customer’s problem. Additional contributors to this stage include the product analyst, who tracks progression of metrics towards pre-determined targets, and the data scientist who monitors how the ML models perform in production, and makes improvements in an iterative manner.
As you can see, traditional product roles like product managers, software engineers, and so on contribute heavily to the process of building AI products. The reason for this is that your customers don’t care about fancy machine learning models; they care about delightful user experiences that allow them shop, learn, travel, connect and create more, but faster and cheaper.
The proven model for delivering these delightful experiences is still the goal-oriented, cross-functional, and autonomous product team. However, this team has a new member: the data scientist. What this means for other members of the team is twofold. First, educate yourself on the basics of machine learning. You don’t need to become an ML expert, but the UX designer for example should know as much about machine learning processes as he does about software engineering. Second, develop the ability to identify customer problems that were unsolvable in the past, but could now be solved with machine learning. For example, you should be able to tell if a customer problem sounds like a prediction problem, or a classification problem, so you can brainstorm with your data scientist on what is possible solution wise.