Free preview
You can read roughly the first 3 minutes of this lesson before upgrading.
The ML Product Development Lifecycle: Your New Best Friend
Hey there, Product Manager extraordinaire! Welcome to the magical world of the ML Product Development Lifecycle. Think of it as the recipe to your grandma's secret cookie formula — each step is crucial to ensuring that your ML product is not just a science project, but a real, impactful product.
1. Problem Definition: The North Star
Before you dive into the deep end of data, you need to know what you're solving. This isn't just about tossing data into a model and praying for the best. Problem Definition is where you set the stage. For instance, Spotify didn't start with "Let's use machine learning!" Instead, they asked, "How can we better recommend music to our users?"
Why this matters for PMs: Clear problem definitions help ensure you're solving the right problem for your users, not just playing around with fancy tech.
What comes next
2. Data Collection: Gathering the Ingredients
Once you know what you're solving, it's time to gather your ingredients — data, glorious data. This step involves collecting, cleaning, and preparing data that your team will use to train models. Think of Google Photos using user-uploaded images to learn what a "dog" is.
Why this matters for PMs: As a PM, you need to ensure the data is relevant, sufficient, and ethical. You'll be the one asking, "Do we have the right data to solve this problem?"
3. Model Development: The Chef in Action
Finish: The ML Product Development Lifecycle
Continue instantly and access the complete breakdown, diagrams, exercises, and downloadable templates from Working With ML Teams.