How we turned around an ML product by looking differently at the data

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This is the GoPractice team.

How we turned around an ML product by looking differently at the data

When you’re training machine learning models in a lab, the goal is to optimize the model for a specific metric. When you’re building real-world ML applications, you must take a broader view of the application, the economics, the end users, and the market to make the right decision.

In this post, we will share a hard-earned lesson in adjusting the economics of machine learning products from our expert’s experience. This can help you avoid similar mistakes and get your ML products working faster.

🔗 https://gopractice.io/skills/how-we-turned-around-an-ml-product-by-looking-differently-at-the-data/