AI Solution Architect
Design enterprise-scale AI solutions with advanced architectures, infrastructure automation, security, governance, and industry-leading practices for maximum impact.
Course Modules
AI System Design Principles
Design thinking, architecture patterns, scalability, reliability, cost optimization, and documentation for AI systems.
Advanced RAG Architectures
Multi-step RAG, graph-based architectures, corrective RAG, self-RAG, and answer verification pipelines.
Vector Database Engineering
Qdrant, Weaviate, Pinecone at scale — comparison, indexing, performance tuning, and hybrid search strategies.
LLM Orchestration & Agents
LangChain, LlamaIndex, multi-agent systems, tool orchestration, planning, and reasoning with AI agents.
MLOps Fundamentals
Experiment tracking with MLflow, data versioning with DVC, feature stores, and model governance.
LLMOps & Prompt Management
Prompt versioning at scale, cost tracking, latency optimization, caching strategies, and guardrails.
CI/CD for AI Systems
Model validation, automated deployment pipelines, rollback, incident response, and ML testing in CI/CD.
Container Orchestration
Kubernetes for ML, GPU scheduling, container optimization, Helm charts, and service mesh for microservices.
Serverless AI Deployment
AWS Lambda, Azure Functions for ML, serverless inference, cost optimization, and function orchestration.
Monitoring & Observability
Prometheus, Grafana, model drift detection, alerting, incident response, and distributed tracing.
Security & Governance
AI security threats, prompt injection defense, data privacy, access control, adversarial testing, and secrets management.
Enterprise AI Architecture
Enterprise patterns, multi-tenant platforms, platform engineering, stakeholder management, and production case studies.
Industry Trends & AI Ecosystem
Model comparisons, open source LLMs, orchestration tools, vector DBs, MLOps tools, and emerging trends.