Multi-Cloud AI Engineer

Multi-Cloud AI Engineer

  • 11 Phases
  • 30 Lessons
  • 6 Hrs
  • Beginner Advanced
Category Cloud computing
Multi-Cloud AI Engineer
Learning Path 30 Lessons 6 Hrs

Start Phase
  • What is a Multi-Cloud AI Engineer? 10:02
  • Why Multi-Cloud for AI? 04:29
  • Roles & Responsibilities 25:18
  • Learning Roadmap 04:47

Start Phase
  • Python Web Automation with Selenium 11:41
  • Go for Cloud & DevOps 17:02
  • Data Structures & Algorithms (Practical) 07:23
  • Networking Fundamentals (TCP/IP, DNS, Load Balancing) 12:04

Start Phase
  • Cloud Service Models (IaaS, PaaS, SaaS) 06:43
  • VPC, Networking & Load Balancers 05:23
  • IAM, Compute, Storage & Security Basics 14:01

Start Phase
  • Supervised & Unsupervised Machine Learning 09:40
  • Neural Networks & Deep Learning Basics 18:40
  • EDA & Feature Engineering 29:59

Start Phase
  • Model Versioning & Experiment Tracking 19:58
  • CI/CD Pipelines for ML 13:57
  • Model Monitoring, Drift & Alerts 07:27

Start Phase
  • AI Services on AWS (SageMaker, Bedrock) 10:58
  • AI Services on Azure (Azure ML, Azure OpenAI) 22:41
  • AI Services on GCP (Vertex AI, BigQuery ML) 03:39
  • Multi-Cloud AI Architecture & Portability 17:06

Start Phase
  • Prompt Engineering (Production-Grade) 08:30
  • LLM Integration & Scaling 07:42

Start Phase
  • Infrastructure as Code (Terraform, ARM, CloudFormation) 20:50
  • Docker & Kubernetes Overview 5:51
  • Kubernetes for AI 06:28

Start Phase
  • IAM & Identity Security for Cloud AI 17:16
  • FinOps & Cost Optimization for AI Workloads 04:10

Start Phase
  • Responsible AI & Governance 06:06

Start Phase
  • Cloud, AI & GenAI Certification Path 22:08

Start your Multi-Cloud AI Engineer journey

Learn at your own pace. Total estimated time 6 hours

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