Introduction:
Artificial Intelligence (AI) applications require a robust and scalable infrastructure to support data processing, storage, model training, deployment, and monitoring. This course introduces the foundational elements of AI infrastructure and operations, focusing on how organizations can design, implement, and manage systems that enable AI at scale. Participants will gain a clear understanding of cloud platforms, data pipelines, computing resources, deployment strategies, monitoring, and governance for AI systems.
General Objectives:
By the end of this course, participants will be able to:
- Understand the core components of AI infrastructure (compute, storage, networking, and data pipelines).
- Evaluate cloud vs. on-premises infrastructure for AI workloads.
- Learn the fundamentals of MLOps (Machine Learning Operations) and DevOps practices for AI.
- Explore tools and platforms for model training, deployment, and monitoring.
- Address operational challenges including scalability, reliability, and cost optimization.
- Understand governance, compliance, and security considerations for AI systems.
Course Outline
Day 1 – Foundations of AI Infrastructure
- Overview of AI systems and lifecycle
- Infrastructure requirements for AI workloads
- Cloud, hybrid, and on-premises environments
- Key technologies: GPUs, TPUs, CPUs, storage systems
Day 2 – Data Infrastructure for AI
- Data ingestion and processing pipelines
- Data lakes vs. data warehouses
- Streaming vs. batch processing
- Tools: Apache Kafka, Spark, Databricks
Day 3 – Compute and Model Training Infrastructure
- Distributed computing for AI
- Resource orchestration with Kubernetes and Docker
- Scaling training with GPUs/TPUs
- Cloud platforms (AWS SageMaker, Azure ML, Google Vertex AI)
Day 4 – AI Operations and Deployment (MLOps)
- Introduction to MLOps practices
- CI/CD pipelines for AI models
- Model serving and deployment strategies (REST APIs, batch inference, edge deployment)
- Monitoring models in production (drift detection, retraining triggers)
Day 5 – Governance, Security, and Future Trends
- Security in AI infrastructure
- Governance, compliance, and ethical AI
- Cost management and optimization strategies
- Emerging trends: AI at the edge, generative AI infrastructure, quantum computing impact