Here’s a 30-day training plan to get ready for deploying and supporting AI on Google Cloud Platform (GCP), assuming you’re working full-time (5 days/week, 8 hours/day). This plan is focused on practical experience with Vertex AI, pretrained models, custom model deployment, and GCP’s data/ML tooling.

🗓️ Week-by-Week GCP AI Learning Plan

📘 Week 1: GCP & AI Foundations

Goal: Understand GCP core services, AI stack, and setup basics.

🧠 Key Topics:

• GCP fundamentals: IAM, Projects, Billing, Networking
• GCP AI Stack overview: Vertex AI, AutoML, BigQuery ML, APIs
• Google AI principles, Responsible AI

📌 Daily Plan:

📚 Resources:

• Google Cloud Fundamentals
• AI and Machine Learning on GCP

🔧 Week 2: Prebuilt Models & AutoML with Vertex AI

Goal: Use and deploy AutoML models for common ML tasks.

📌 Daily Plan:

📚 Resources:

• Vertex AI AutoML
• Vertex AI Workbench

🧠 Week 3: Custom Training, Model Deployment & BigQuery ML

Goal: Train and deploy custom models on GCP.

📌 Daily Plan:

📚 Resources:

• Vertex AI custom training
• BigQuery ML

🤖 Week 4: Pipelines, Generative AI & Final Project

Goal: Practice orchestration, GenAI APIs, and full AI solution deployment.

📌 Daily Plan:

📚 Resources:

• Vertex AI Pipelines
• Generative AI on GCP
• LangChain + Vertex AI

🏁 Week 5: Review, Optimize, Certify

Goal: Refactor, optimize, and prepare for certification if desired.

✅ Suggested Deliverables:

• Pre-trained AI API demo (Vision, Text)
• AutoML model (image or text)
• Custom-trained and deployed ML model
• BigQuery ML example
• GenAI demo app with prompt tuning
• End-to-end AI pipeline

Trello Board: GCP AI 30-Day Training Plan

📘 List: Week 1 – GCP & AI Foundations

Cards:

• Set up GCP project, billing, and IAM roles
• Explore Cloud Console, Cloud Shell, and Cloud SDK
• Understand Cloud Storage, VPC, Compute Engine basics
• Intro to Vertex AI: features and dashboard
• Use pre-trained APIs: Vision, Natural Language, Speech-to-Text
• Explore GCP’s Responsible AI Toolkit
• Enable Cloud Monitoring and Logging for AI APIs

🔧 List: Week 2 – AutoML & Vertex AI Basics

Cards:

• Train image classifier with AutoML Vision
• Train structured model with AutoML Tables
• Perform text classification with AutoML NLP
• Deploy AutoML model to endpoint
• Run batch and online predictions
• Monitor model performance and quota usage
• Integrate AutoML model into an app

🧠 List: Week 3 – Custom Models & BigQuery ML

Cards:

• Launch Vertex AI Workbench and run sample notebook
• Train Keras/sklearn model locally and in the cloud
• Register model in Vertex AI Model Registry
• Deploy custom model to endpoint
• Query and train models with BigQuery ML (linear/logistic regression)
• Export BigQuery ML predictions to table
• Evaluate models with confusion matrix and AUC

🤖 List: Week 4 – Pipelines, GenAI, Capstone

Cards:

• Build Vertex AI Pipeline (training + deployment steps)
• Add data preprocessing to pipeline with TFX or Kubeflow
• Set up CI/CD for ML models using Cloud Build
• Try prompt tuning in Generative AI Studio
• Call PaLM/Gemini APIs from Python or Postman
• Build GenAI-powered chatbot/app
• Plan and start building capstone AI solution

🎓 List: Week 5 – Optimization & Review

Cards:

• Finalize and test AI capstone project end-to-end
• Add monitoring and alerting to deployed models
• Review cost management and pricing for AI solutions
• Refactor pipelines and models for reusability
• Prepare for Google Cloud ML Engineer certification
• Take mock exam or Google Skills Boost challenge lab