Moor Metrics

𝗛𝗼𝘄 𝘁𝗼 𝗠𝗶𝘀𝘀 𝗢𝘂𝘁 𝗼𝗻 𝗔𝗜 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻

Wait for “perfect” data before starting your first neural network.

𝗕𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗕𝗲𝘁𝘁𝗲𝗿 𝗮𝘁 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀

𝘐𝘯 𝘵𝘩𝘦 𝘣𝘦𝘨𝘪𝘯𝘯𝘪𝘯𝘨, 𝘥𝘰𝘯’𝘵 𝘫𝘶𝘥𝘨𝘦 𝘺𝘰𝘶𝘳𝘴𝘦𝘭𝘧 𝘰𝘯:
- How many predictions you’ve made
- How perfect your models seem
- How impressive your metrics look

𝘐𝘯𝘴𝘵𝘦𝘢𝘥, 𝘫𝘶𝘥𝘨𝘦 𝘺𝘰𝘶𝘳𝘴𝘦𝘭𝘧 𝘰𝘯:
- Choosing the right problem to solve
- Evaluating performance honestly
- Improving your feature engineering

𝙍𝙚𝙨𝙪𝙡𝙩𝙨 𝙖𝙡𝙬𝙖𝙮𝙨 𝙡𝙖𝙜 𝙗𝙚𝙝𝙞𝙣𝙙 𝙝𝙖𝙗𝙞𝙩𝙨.
𝙁𝙤𝙘𝙪𝙨 𝙤𝙣 𝙥𝙧𝙤𝙘𝙚𝙨𝙨𝙚𝙨 𝙛𝙞𝙧𝙨𝙩, 𝙤𝙪𝙩𝙘𝙤𝙢𝙚𝙨 𝙡𝙖𝙩𝙚𝙧.

𝐓𝐡𝐞 𝐄𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐨𝐟 𝐚 𝐆𝐨𝐨𝐝 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐧𝐚𝐢𝐫𝐞

𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫: Questions are written randomly.
𝐈𝐧𝐭𝐞𝐫𝐦𝐞𝐝𝐢𝐚𝐭𝐞: Questions are written to collect responses.
𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝: Questions are written to test hypotheses.
𝐌𝐚𝐬𝐭𝐞𝐫: Questions are written to reveal insights that drive decisions.

𝐅𝐫𝐨𝐦 𝐒𝐭𝐚𝐭𝐢𝐜 𝐭𝐨 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜
How to present business insights:

𝐎𝐥𝐝 𝐰𝐚𝐲
- Static bar charts
- Dense Excel sheets
- Hard-to-read PowerPoints
- Endless tables

𝐍𝐞𝐰 𝐰𝐚𝐲
- Interactive dashboards
- Clear visual storytelling
- Drill-down capabilities
- Real-time updates

𝐈𝐧𝐬𝐢𝐠𝐡𝐭 > 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧

𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐢𝐧 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠

Training of deep models without sufficient data is a great way for overfitting to be experienced and resources to be wasted.
Avoid.

99% of AI-driven solutions we encounter don’t prioritise what matters most:
1. Data quality
2. Scalability of the model
3. Continuous monitoring and improvement
Too much focus on algorithms.
Not enough focus on outcomes.

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