====== 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**. ---- **πŸ—“οΈ Week-by-Week GCP AI Learning Plan** ---- **πŸ“˜ 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 **πŸ“Œ 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 | πŸ“š Resources: * Google Cloud Fundamentals * AI and Machine Learning on GCP ---- **πŸ”§ Week 2: Prebuilt Models & AutoML with Vertex AI** **Goal:** Use and deploy AutoML models for common ML tasks. **πŸ“Œ 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 | πŸ“š Resources: * Vertex AI AutoML * Vertex AI Workbench ---- **🧠 Week 3: Custom Training, Model Deployment & BigQuery ML** **Goal:** Train and deploy custom models on GCP. **πŸ“Œ 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 | πŸ“š Resources: * Vertex AI custom training * BigQuery ML ---- **πŸ€– Week 4: Pipelines, Generative AI & Final Project** **Goal:** Practice orchestration, GenAI APIs, and full AI solution deployment. **πŸ“Œ 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 | πŸ“š 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 | | ---- **βœ… 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** ---- **πŸ“˜ 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 ---- **πŸ”§ 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 ---- **πŸ€– 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 ---- **πŸŽ“ 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 [[wiki:ai:home-page|AI Cloud Managed Services Policies and Procedures]]