🍲 Enchanted Recipe for a Machine Learning Model on Google Cloud 🍲

Ingredients:

• 1 cup of Clean Data (extracted and transformed in BigQuery)
• 2 tablespoons of Data Visualization (prepared in Data Studio)
• 3 teaspoons of Preprocessing (normalization, standardization, and feature selection in AI Platform Notebooks)
• A pinch of Adjusted Hyperparameters (slow-cooked in Vertex AI)
• A touch of Evaluation and Testing (for that “final seasoning”)

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Magic Preparation Instructions:

Step 1: Gathering Ingredients in BigQuery 🥄

• Extract and transform your data in BigQuery. This is the first step to ensure fresh, clean data.
• Use SQL queries to filter and clean your data, just like selecting the best fruits from the market.

Step 2: Visual Preparation in Data Studio 🍉

• Bring your data into Data Studio to visualize and better understand its shape and content.
• Create charts and tables to uncover hidden patterns, as if your ingredients are whispering to you about how to combine them.

Step 3: Mise en Place in AI Platform Notebooks 🍴

• Preprocess the data in AI Platform Notebooks, where the ingredients are carefully portioned and prepared.
• Normalize and standardize the data, and select the finest features as though picking the perfect spices to flavor the final dish.

Step 4: Hyperparameter Cooking in Vertex AI 🍲

• Transfer your data into Vertex AI and start mixing! Adjust hyperparameters with precision, like calculating the exact cooking time.
• Let the model cook patiently, training until it reaches its best version.

Step 5: Seasoning Test (Evaluation and Testing) 🌶️

• Serve the model on a validation dataset to test its "flavor" (performance).
• Adjust and evaluate metrics like precision and recall, aiming for a perfect balance.

Step 6: Serving the Dish in Cloud Run 🍛

• Deploy the model in AI Platform Prediction or Cloud Run, creating an endpoint so every customer can enjoy it.
• Set up API authentication so only authorized tasters get a sample.

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Final Garnish ✨

• Monitor and optimize the model in production with Cloud Monitoring, ensuring every prediction is well “seasoned.”
• Scale and adjust so every customer, in real-time or batch, receives the best service.

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And Voilà! 🎩

Your Machine Learning model on Google Cloud is ready and waiting to be enjoyed.