30 Day Plan to Deploy AI in GCP

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:

Day	Topics	Hands-on
1	GCP Console, Projects, IAM	Set up GCP project, roles, billing
2	Cloud Storage, Compute, VPCs	Upload data, launch VM, configure buckets
3	Intro to Vertex AI	Explore Vertex AI dashboard & notebooks
4	AI building blocks (Vision, NLP, Speech)	Use pre-trained APIs via Cloud Functions
5	Responsible AI & ML workflow	Enable explainability, test data bias tools

📚 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:

Day	Topics	Hands-on
6	Vertex AI AutoML Vision	Train and deploy image classifier
7	Vertex AI AutoML Tables	Predict values using structured data
8	Vertex AI AutoML Text/NLP	Sentiment analysis, entity recognition
9	Endpoints & predictions	Deploy AutoML model to endpoint
10	Monitoring & logging	Enable monitoring, test predictions, quotas

📚 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:

Day	Topics	Hands-on
11	Jupyter in Vertex AI Workbench	Launch notebook instance, train model
12	Custom Python models	Use Keras/sklearn to train and upload
13	Custom model deployment	Deploy using Vertex AI Model Registry
14	BigQuery ML overview	Train ML model on tabular data via SQL
15	BigQuery ML deep dive	Evaluate model, export predictions to table

📚 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:

Day	Topics	Hands-on
16	Vertex AI Pipelines	Build a training and deployment pipeline
17	ML Ops tools (CI/CD, model monitoring)	Integrate with Cloud Build, logging
18	GenAI Studio + PaLM API	Prompt tuning with PaLM and Gemini APIs
19	Deploy GenAI-enabled chat/app	Integrate LLM with app or chatbot
20	Capstone: Deploy full AI solution	Includes custom model + prediction endpoint + dashboard

📚 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.

Day	Topics	Hands-on
21	Refactor and document capstone project
22	Implement CI/CD for ML models using Cloud Build
23	Add monitoring/alerting with Cloud Monitoring
24	Review Vertex AI limitations, pricing, cost controls
25	Take mock exam or prep for Google Cloud ML Engineer certification

✅ 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

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