AI Infrastructure and Operations Fundamentals

Description

• 📘 Course Title:

  AI Infrastructure and Operations Fundamentals

  📅 Duration:

  6–8 Weeks (can be adjusted based on pace)

  🎯 Target Audience:

  • IT administrators

  • Cloud engineers

  • Data engineers

  • DevOps professionals

  • Beginners aiming to enter AI/ML infrastructure roles

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  🔹 Module 1: Introduction to AI Infrastructure

  Topics:

  • What is AI infrastructure?

  • Types of AI workloads (training vs inference)

  • Basic architecture of AI systems

  • Overview of compute, storage, and network needs for AI

  Outcomes:

  • Understand core infrastructure requirements for AI

  • Recognize different types of AI system designs

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  🔹 Module 2: Hardware Components for AI

  Topics:

  • CPUs vs GPUs vs TPUs

  • GPU memory, processing cores, and parallelism

  • High-performance networking (InfiniBand, NVLink)

  • Storage solutions (NVMe, SSDs, distributed file systems)

  Hands-On Labs:

  • Benchmarking performance with and without GPUs

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  🔹 Module 3: Software Stack for AI Operations

  Topics:

  • AI/ML frameworks (TensorFlow, PyTorch, JAX)

  • OS and drivers (Linux, CUDA, cuDNN)

  • Containerization with Docker and Podman

  • Using Kubernetes for AI workloads

  Hands-On Labs:

  • Run a simple training job using Docker + TensorFlow

  • Set up GPU support in containers

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  🔹 Module 4: Data Pipeline and Management

  Topics:

  • Data ingestion, transformation, and storage

  • Data lakes and warehouses

  • ETL tools (Apache Airflow, Kafka basics)

  • Versioning datasets (DVC, Delta Lake)

  Hands-On Labs:

  • Create a basic data pipeline with Airflow

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  🔹 Module 5: Model Training and Inference Infrastructure

  Topics:

  • Distributed training techniques

  • Hyperparameter tuning and resource optimization

  • Inference serving architecture (REST, gRPC)

  • Tools: TensorFlow Serving, TorchServe, ONNX Runtime

  Hands-On Labs:

  • Deploy a model with TensorFlow Serving

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  🔹 Module 6: Cloud and On-Premise Deployment Options

  Topics:

  • AI in public cloud (AWS, Azure, GCP AI offerings)

  • On-premise solutions (NVIDIA DGX, OpenShift AI)

  • Hybrid and edge AI infrastructure

  • Cost optimization strategies

  Hands-On Labs:

  • Compare cloud AI services (e.g., SageMaker vs Vertex AI)

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  🔹 Module 7: MLOps and AI DevOps Fundamentals

  Topics:

  • CI/CD pipelines for ML (MLFlow, Kubeflow)

  • Monitoring models and infrastructure

  • Model drift and retraining

  • Infrastructure as Code (Terraform, Helm)

  Hands-On Labs:

  • Build a simple ML CI/CD pipeline with MLFlow

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  🔹 Module 8: Security, Compliance, and Scalability

  Topics:

  • AI infrastructure security essentials

  • Role-based access control (RBAC)

  • Data privacy regulations (GDPR, HIPAA)

  • Scaling infrastructure for large models (LLMs)

  Hands-On Labs:

  • Set up RBAC on a Kubernetes AI cluster

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  🔹 Capstone Project

  Project Idea:
  Deploy a full AI workflow — data ingestion, model training, serving, and monitoring — using containerized infrastructure and cloud-based tools.

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  📜 Certification & Evaluation

  • Weekly quizzes

  • Final hands-on project submission

  • Completion certificate (optional badge for cloud/GPU setup)