πŸ“ŒRule #2: Metrics Matter! πŸ“Šβœ¨


When diving into machine learning, it’s essential to start by designing and implementing metrics. Why? Let me share some key reasons:

1️⃣ Gain Permission: By tracking as much as possible in your current system upfront, you can easily gain permission from system users. This early involvement sets the stage for collaboration and buy-in. πŸ‘₯πŸ—οΈ

2️⃣ Capture Historical Data: Anticipating future concerns? Don’t wait! Start collecting historical data now. It’s crucial for building a solid foundation and understanding the system’s behavior over time. πŸ“ˆβ³

3️⃣ Future-proof Your Design: It’s essential to design your system with metric instrumentation in mind. Why? Because it saves you from the hassle of searching through logs to instrument your metrics later on. Plan ahead for smoother operations! πŸ“‹πŸ”§

4️⃣ Identify Patterns: Tracking metrics allows you to observe what changes and what remains constant. For instance, suppose you aim to optimize for “oneday active users.” During early system manipulations, you might notice that significant alterations to the user experience don’t noticeably impact this metric. These insights help you fine-tune your approach and understand user behavior. πŸ”„πŸ‘₯πŸ”

Remember, metrics lay the foundation for success in machine learning. They guide decision-making, highlight performance trends, and provide valuable insights into system behavior. So, embrace the power of metrics and unlock the potential of your machine learning journey! πŸ’‘πŸ’ͺ

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