Google AI Cloud Engineer Course & Curriculum

NCPL's hands-on Google AI Cloud Engineer training — full curriculum, real projects, certification prep, and job placement support. We train, mentor and place you.

Module 1: AI, ML & Cloud Foundations

5 hours: AI vs ML vs DL, enterprise use cases, GCP AI/ML ecosystem, responsible AI

  • AI vs ML vs DL vs Generative AI
  • Enterprise AI use cases (North America)
  • GCP AI/ML ecosystem overview
  • Responsible AI & ethics (Google principles)

Module 2: GCP AI & ML Ecosystem Overview

5 hours: Vertex AI vs pre-built APIs, managed vs custom training, IAM fundamentals

  • Vertex AI vs pre-built AI APIs
  • Managed AI vs custom model training
  • GCP project & IAM fundamentals
  • Cost models & quotas
  • Reference architectures

Module 3: Generative AI on GCP – Vertex AI & Gemini

8 hours: Gemini models, prompt engineering, embeddings, RAG concepts

  • Vertex AI architecture
  • Gemini models (text, multimodal)
  • Prompt engineering (enterprise patterns)
  • Safety filters & content moderation
  • Embeddings & similarity search
  • RAG concepts on GCP

Module 4: Natural Language AI Services

6 hours: Cloud Natural Language API, sentiment/entity analysis, classification

  • Cloud Natural Language API
  • Sentiment analysis
  • Entity analysis
  • Syntax & classification
  • Language detection

Module 5: Speech & Conversational AI

5 hours: Speech-to-Text, Text-to-Speech, streaming, multilingual voice apps

  • Speech-to-Text
  • Text-to-Speech
  • Streaming vs batch speech
  • Multilingual voice applications
  • Conversational AI use cases

Module 6: Vision & Document AI

6 hours: Vision API, OCR, Document AI processors, enterprise workflows

  • Vision API (image & object detection)
  • OCR & handwriting recognition
  • Document AI processors
  • Invoice, resume & ID extraction
  • Enterprise document workflows

Module 7: Enterprise Search & RAG on GCP

5 hours: Vertex AI Search, vector embeddings, RAG architecture, search tuning

  • Vertex AI Search / Agent Builder
  • Vector embeddings & similarity search
  • RAG architecture on GCP
  • Indexing PDFs & documents
  • Search relevance tuning

Module 8: Custom ML with Vertex AI

5 hours: Vertex AI Workbench, custom models, ML pipelines, MLOps basics

  • Vertex AI Workbench
  • Data prep & feature engineering
  • Training custom models
  • Model registry & endpoints
  • ML pipelines & MLOps basics

Module 9: Security, Governance & Deployment

5 hours: IAM, secrets, monitoring, CI/CD for AI, governance & compliance

  • IAM & service accounts
  • Secrets Manager & encryption
  • Monitoring with Cloud Monitoring
  • Cost optimization strategies
  • CI/CD for AI workloads
  • AI governance & compliance