Combined Cloud Managed Services

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====== 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**.

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**🗓️ Week-by-Week GCP AI Learning Plan**

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**📘 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

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**🔧 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

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**🧠 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

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**🤖 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

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**🏁 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 |   |

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**✅ 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**

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**📘 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

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**🔧 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

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**🧠 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

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**🤖 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

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**🎓 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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