Several organizations have started their journey toward developing their ML models. The journey can be challenging but to make it easy @Deepak Bhardwaj has outlined the following steps with a structured approach towards the development of AI models.
🔘 𝐃𝐚𝐭𝐚 𝐈𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧: Collect data from various sources.
🔘 𝐃𝐚𝐭𝐚 𝐏𝐫𝐞𝐩𝐚𝐫𝐚𝐭𝐢𝐨𝐧: Preparing data for analysis. ↳ Validate: Ensure data is correct. ↳ Clean: Remove errors and inconsistencies. ↳ Standardise: Make data uniform. ↳ Curate: Organise data effectively. ↳ Anonymise: Protect personal information.
🔘 𝐃𝐚𝐭𝐚 𝐋𝐚𝐤𝐞 & 𝐃𝐞𝐥𝐭𝐚 𝐋𝐚𝐤𝐞: Store raw and processed data.
🔘 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: Create useful data features. ↳ Extract Features: Select essential data points. ↳ Split Dataset: Divide data for training and testing.
🔘 𝐌𝐨𝐝𝐞𝐥 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠: Develop and refine models. ↳ Code: Write algorithms. ↳ Train: Teach models using data. ↳ Evaluate model performance. ↳ Optimise: Improve model accuracy.
🔘 𝐌𝐨𝐝𝐞𝐥 𝐑𝐞𝐠𝐢𝐬𝐭𝐫𝐲 & 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭: Manage and deploy models. ↳ Package: Bundle models for deployment. ↳ Containerise: Use containers for consistency. ↳ Deploy: Implement models into production.
