Model Online / On-Site
Duration
Modules 6 modules

Course Objective & Scope

When every team builds bespoke data pipelines, vector stores and direct LLM integrations from scratch, organisations face duplicated spend, inconsistent security and slow paths to production. This advanced academy covers how to architect and operate a unified Data and AI Platform that supports both predictive ML and generative/agentic AI workloads at enterprise scale.

Topics span modern lakehouse and unstructured data pipelines, vector and feature stores with fine-grained access control, centralised AI Gateways (routing, rate limiting, PII masking, cost tracking), model serving on Kubernetes with vLLM/Triton/KServe, MLOps/LLMOps CI/CD automation and AI FinOps. Participants produce a reference architecture and platform roadmap tailored to their environment.

Who Should Attend?

  • Data platform, ML platform and cloud infrastructure architects and engineers
  • Data engineers, MLOps/LLMOps engineers and SRE/DevOps specialists
  • Engineering managers and technical leads responsible for enterprise data and AI platforms
  • Prerequisite: Working knowledge of data engineering, containers/Kubernetes and cloud or hybrid infrastructure

Key Takeaways

  • Architect a unified data foundation serving analytics, predictive ML and unstructured GenAI workloads
  • Build enterprise ingestion, chunking, embedding and vector-indexing pipelines that preserve source-level access controls (RBAC/ABAC)
  • Design a central AI Gateway for multi-model routing, semantic caching, guardrails and quota enforcement
  • Standardise MLOps and LLMOps lifecycle automation across model registries, evaluation gates and deployment pipelines
  • Optimize GPU utilisation, inference latency and token spend through disciplined AI FinOps practices

Curriculum

Modern Data Architecture and AI-Ready Data Foundations
  • Lakehouse tables (Iceberg/Delta), data contracts, automated quality checks and lineage
  • Unifying structured tables and unstructured documents/media for ML and GenAI pipelines
Enterprise Context, Feature Stores and Vector Infrastructure
  • Online/offline feature stores and scalable vector database patterns (pgvector, Qdrant, Milvus, OpenSearch)
  • Incremental ingestion pipelines and enforcing source document ACLs/RBAC at retrieval time
Centralised AI Gateway and Multi-Model Routing
  • Unified gateway pattern across cloud providers (Azure AI Foundry, Bedrock, Vertex AI) and self-hosted endpoints
  • Rate limiting, automatic failover, PII redaction, semantic caching and per-team budget quotas
Model Serving, GPU Infrastructure and Inference Optimisation
  • Serving open-weight and custom models on Kubernetes using vLLM, SGLang, Triton and KServe
  • Continuous batching, quantization, KV-cache management and GPU partitioning strategies
Unified MLOps / LLMOps Pipelines and Observability
  • Experiment tracking, model/prompt registries, automated eval gates and canary rollouts
  • Monitoring data drift, TTFT/TPOT latency, quality degradation and OpenTelemetry traces
AI FinOps, Golden Paths and Reference Architecture Capstone
  • Token and GPU unit economics, model routing for cost reduction and showback/chargeback reporting
  • Designing the organisation's target Data & AI Platform architecture and self-service developer golden paths

Other Courses in This Category

  • Human Skills for the AI Era Academy

    Multi-module academy for durable human capabilities in the AI era: problem framing, critical evaluation of AI outputs, ethical judgement, stakeholder storytelling, facilitation and learning agility.

    Corporate Academies & Programme Models Corporate
    Live Course
    6 modules
    View
  • Executive AI Leadership Academy

    Multi-module AI leadership academy for executives and business unit heads: AI strategy, use-case portfolio and unit economics, operating model, EU AI Act/ISO 42001 governance and an enterprise AI roadmap.

    Corporate Academies & Programme Models Corporate
    Live Course
    6 modules
    View
  • AI Fluency and Business Productivity Academy

    Comprehensive AI literacy and productivity academy for business teams: prompt and context engineering, document and data analysis, custom assistants, verification guardrails and a department workflow project.

    Corporate Academies & Programme Models Corporate
    Live Course
    6 modules
    View
  • Agentic Automation Academy

    Transitioning from brittle rule-based automation to agentic workflows: process redesign, Copilot Studio/n8n/LangGraph and MCP tool integration, human-in-the-loop guardrails and a working enterprise prototype.

    Corporate Academies & Programme Models Corporate
    Live Course
    6 modules
    View
  • Generative AI Engineering Bootcamp

    End-to-end production GenAI engineering bootcamp: LLM APIs and open-weight models, advanced RAG and reranking, LangGraph agents, LoRA fine-tuning, automated evaluation and LLMOps/vLLM serving.

    Corporate Academies & Programme Models Corporate
    Live Course
    6 modules
    View
  • AI-Native Software Engineering Academy

    Transforming the full software development lifecycle (SDLC) into an AI-native discipline: repo context rules, IDE/CLI coding agents, legacy modernisation, automated testing, DevSecOps and team governance.

    Corporate Academies & Programme Models Corporate
    Live Course
    6 modules
    View

Get in touch