Cloud AI Platforms

Master AI services across the three major cloud providers — Azure OpenAI, AWS Bedrock, and Google Vertex AI. Learn to build, train, deploy, and scale AI solutions in the cloud.

Platforms Covered

Comprehensive training across all three major cloud AI ecosystems

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Microsoft Azure OpenAI

Best for: Microsoft ecosystem users, enterprise

Deploy GPT-4, DALL-E, Whisper, and other OpenAI models on Azure with enterprise security and compliance.

  • Azure OpenAI Service setup
  • GPT-4 & embedding APIs
  • Azure Cognitive Services
  • Azure ML Studio & Pipelines
  • Private endpoint & compliance
Azure OpenAI Azure ML Cognitive Services
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AWS Bedrock & SageMaker

Best for: AWS users, startups, scale-up teams

Access foundation models from Anthropic, Meta, Amazon, and others via Bedrock — and build production ML with SageMaker.

  • AWS Bedrock & foundation models
  • SageMaker training & endpoints
  • Lambda + API Gateway AI APIs
  • Step Functions & ML Pipelines
  • IAM, VPC & security
AWS Bedrock SageMaker Lambda
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Google Vertex AI

Best for: Data teams, analytics, Gemini integration

Build and deploy ML models with Google's unified AI platform — including Gemini, AutoML, and Vertex Pipelines.

  • Vertex AI Workbench & AutoML
  • Gemini API integration
  • BigQuery ML
  • Vertex Pipelines & MLOps
  • Model Garden & fine-tuning
Vertex AI Gemini BigQuery

Who Is This Program For?

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Cloud Engineers

Extend your cloud skills into AI — deploy and manage production AI services on your preferred platform.

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DevOps & MLOps

Build CI/CD pipelines for ML models, manage deployments, and monitor model performance at scale.

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Software Engineers

Integrate cloud AI APIs into your applications — from text generation to vision and speech recognition.

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IT & Security Teams

Understand cloud AI governance, data residency, compliance, and access control in enterprise environments.

Detailed Curriculum

12-module program covering all three cloud AI ecosystems end-to-end

1. Intro to Cloud AI Platforms
  • Azure vs AWS vs GCP comparison
  • Service models: IaaS, PaaS, SaaS
  • When to use which platform
2. Environments & Access Setup
  • Account setup on all three platforms
  • IAM & permissions management
  • Security best practices
3. Foundation Models & LLM APIs
  • GPT-4 on Azure, Claude on Bedrock
  • Gemini on Vertex AI
  • API integration & rate limits
4. Data Management in the Cloud
  • S3, Azure Blob, GCS
  • BigQuery, Redshift, Synapse
  • Data pipelines & ETL
5. AutoML Solutions
  • Azure AutoML
  • SageMaker Autopilot
  • Vertex AutoML
6. Custom Model Training
  • Training jobs & compute
  • Hyperparameter tuning
  • Distributed training
7. Model Deployment & Endpoints
  • Real-time vs batch inference
  • REST API endpoints
  • Versioning & A/B testing
8. ML Pipelines & Automation
  • Azure ML Pipelines
  • AWS Step Functions
  • Vertex Pipelines (Kubeflow)
9. Advanced AI Services
  • NLP & vision APIs
  • Speech & translation services
  • Document intelligence
10. Security, Compliance & Governance
  • GDPR, HIPAA, SOC 2
  • Data encryption & residency
  • Model access governance
11. MLOps & Monitoring
  • CI/CD for ML models
  • Model drift detection
  • Logging & alerting
12. Capstone Project
  • End-to-end cloud AI solution
  • Multi-platform deployment
  • Architecture review & certification

Technologies & Certifications

This program prepares you for major cloud AI certifications

🔵 Azure AI Engineer (AI-102) 🟠 AWS Certified ML Specialty 🟢 Google Professional ML Engineer ☁️ Azure Data Scientist (DP-100) 🔶 AWS Solutions Architect 📊 Google Cloud Associate

How You'll Learn

Hands-on labs on real cloud environments — not simulations

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Live Lab Sessions

Hands-on exercises in real Azure, AWS, and GCP environments

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Live Online Classes

Instructor-led virtual sessions with screen sharing and Q&A

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Recorded Access

All sessions recorded for review and revision at your own pace

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Mock Exams

Certification exam prep with practice tests and guidance