Complete training for AI infrastructure: from zero to one hundred
The Complete Guide to AI Infrastructure: From Zero to 100 is the ultimate, end-to-end program that helps participants master the infrastructure needed for AI. Whether you’re an aspiring AI engineer, data scientist, or machine learning expert, this course takes you from the very basics of Linux, cloud computing, and GPUs to more advanced topics like distributed learning, Kubernetes orchestration, machine learning operations (MLOps), observability, and deploying AI at the edge. In just 52 weeks, participants will progress from launching their first GPU virtual machine to designing and delivering a complete, production-ready, enterprise-grade AI infrastructure system. This comprehensive curriculum ensures that participants gain both the theoretical foundation and practical skills needed to succeed in the fast-paced and evolving world of AI infrastructure. The course starts with the basics: what AI infrastructure is, why it matters, and how CPUs, GPUs, and TPUs (tensor processing units) power modern AI workloads. Participants learn Linux fundamentals, explore cloud infrastructure on AWS, Google Cloud, and Azure, and gain the confidence to launch GPU compute instances. From there, they dive into containerization with Docker, orchestration with Kubernetes, and automation with Helm charts—skills every AI engineer should master.
In The Complete Guide to AI Infrastructure: Zero to Hero, you’ll learn how to design and implement a complete, ready-to-use AI infrastructure system.
Table of contents of The Complete Guide to AI Infrastructure: Zero to Hero:
- Introduction to the Complete Guide to AI Infrastructure from Zero to Hero
- Week 1: Introduction to AI Infrastructure
- Week 2: Linux Basics for AI Engineers
- Week 3: Cloud Infrastructure Basics
- Week 4: Containerization Basics
- Week 5: Kubernetes Fundamentals
- Week 6: Data Storage for AI
- Week 7: In-depth review of GPU hardware
- Week 8: Basics of Distributed Learning
- Week 9: Automating Workflows and Testing Tracking
- Week 10: CICD for AI models
- Week 11: Advanced Kubernetes for AI
- Week 12: Resource and Cost Optimization
- Week 13: Networking for AI Systems
- Week 14: Model Serving Basics
- Week 15: Advanced Model Presentation
- Week 16: Observability in AI Infrastructure
- Week 17: Model Drift and Data Drift
- Week 18: AI Security and Compliance
- Week 19: Reliability and High Availability
- Week 20: Multi-Cloud AI Infrastructure
- Week 21: Edge AI Infrastructure Basics
- Week 22: Optimizing AI for Edge Devices
- Week 23: Mobile AI Infrastructure
- Week 24: Data Pipelines for AI at Scale
- Week 25: Generative AI Infrastructure – Basics
- Week 26: Advanced Generative AI Infrastructure
- Week 27: Infrastructure for Computer Vision at Scale
- Week 28: Infrastructure for Natural Language Processing (NLP) at Scale
- Week 29: Infrastructure for Multimodal AI
- Week 30: Infrastructure for Reinforcement Learning
- Week 31: Large-Scale Training – Basics
- Week 32: Large-Scale Training – Advanced
- Week 33: Enterprise MLOps – Fundamentals
- Week 34: Enterprise MLOps – Advanced
- Week 35: Optimization Techniques – Basics
- Week 36: Optimization Techniques – Advanced
- Week 37: Federated Learning Infrastructure
- Week 38: Privacy-Preserving AI
- Week 39: AI Infrastructure Security – Advanced
- Week 40: Multi-Tenant AI Infrastructure
- Week 41: AI Infrastructure for Startups
- Week 42: Artificial Intelligence Infrastructure for Enterprises
- Week 43: Infrastructure for Real-Time AI
- Week 44: Infrastructure for Autonomous Systems
- Week 45: Artificial Intelligence Infrastructure – Case Studies
- Week 46: The Future of AI Infrastructure
- Week 47: Capstone Prep – Review
- Week 48: Final Project – Problem Definition
- Week 49: Final Project – First Phase of Implementation
- Week 50: Final Project – Second Phase of Implementation
- Week 51: Final Project – Finalization
- Week 52: Final Project – Presentation and Graduation
