𝗪𝗵𝗮𝘁 𝗪𝗲 𝗟𝗲𝗮𝗿𝗻𝗲𝗱 𝗙𝗿𝗼𝗺 𝗙𝗮𝗶𝗹𝗲𝗱 𝗔𝗜 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀

𝗪𝗵𝗮𝘁 𝗪𝗲 𝗟𝗲𝗮𝗿𝗻𝗲𝗱 𝗙𝗿𝗼𝗺 𝗙𝗮𝗶𝗹𝗲𝗱 𝗔𝗜 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀

AI is popular. Many projects fail. Bad planning kills more projects than bad tech. Here are six lessons to save you money.

Vague goals kill projects. One pharma client wanted better trials. They did not define what better meant. The model was useless.

• Use SMART goals.
• Be specific.

Bad data ruins AI. A retail client had years of sales data. The data was messy. The model failed.

• Clean data beats big data.
• Check your data early.

Complex models fail often. A healthcare project used a complex CNN. It was slow. Doctors did not trust it. A simple Random Forest worked better.

• Start simple.
• Use simple tools first.

Notebooks are not production. An e-commerce tool crashed under high traffic. It lacked scale.

• Plan for production on day one.
• Use Docker and Kubernetes.

AI is not set and forget. A finance model failed when markets shifted. The data drifted.

• Automate your retraining.
• Monitor for drift.

Tech needs trust. Bank staff ignored a fraud model. They did not understand the alerts.

• Train your users.
• Be open.

Your road to success:

• Set SMART goals.
• Clean your data.
• Start simple.
• Plan for scale.
• Monitor drift.
• Train users.

Source: https://dev.to/capestart/what-weve-learned-from-failed-ai-projects-so-you-dont-have-to-4kmf Optional learning community: https://t.me/GyaanSetuAi