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Whitestone International College of Innovation delivers quality-assured, standards-aligned programmes that integrate academic rigour, industry relevance, and digital fluency to develop principled leaders who deliver measurable impact.

Courses

Professional Diploma in Applied AI Engineering & MLOps

Course Overview

The Professional Diploma in Applied AI Engineering & MLOps prepares engineers and technical
leaders to design, build, deploy, and operate AI/ML systems reliably at scale. Blending software
engineering rigor with the ML lifecycle, the programme covers data pipelines, model development,
reproducible experiments, automated deployment, realtime serving, monitoring, governance,
safety, and cost control—so teams move from notebooks to production with confidence.

Why This Course is Important?

Productiongrade AI: Most models never ship; robust MLOps bridges the gap from prototype to reliable service.
Speed + safety: Automated pipelines accelerate delivery while embedding testing, security, and governance.
Business impact: Observable, costefficient ML systems improve uptime, ROI, and stakeholder trust.
Career lift: Equips professionals for highdemand roles in AI engineering, platform/ML infra, and data products.

Learning Outcomes

By the end of this programme, participants will be able to:

Design endtoend ML architectures (ingest → feature store → training → registry → serving → monitoring).
Build reproducible pipelines with data/version control, experiment tracking, and artifact management.
Automate CI/CD/CT for ML with testing gates (unit, integration, data/label quality, bias & drift checks).
Deploy models to batch/online/streaming targets (APIs, jobs, eventdriven) using containers and orchestration.
Monitor performance, data quality, drift, and costs; implement rollback, canary, and shadow deployments.
Apply AI risk, security, compliance, and governance principles across the ML lifecycle.
Optimise inference (quantisation, distillation, ONNX/TensorRT) and manage GPU/accelerator workloads efficiently.

Target Audience

Entry Requirements

Programme Structure & Modules

Module 1 – ML Systems Architecture & the MLOps Lifecycle

Reference architectures; batch vs online vs streaming
Components: data lake/warehouse, feature store, model registry, serving, observability
Build vs buy tradeoffs and platform patterns

Module 2 – Data Engineering for ML

Data ingestion (batch/stream), schema management, and quality checks
Data/feature versioning (e.g., DVC/GitLFS) and lineage
Great Expectations/Deequ for validations; privacy/PII handling and anonymisation

Module 3 – Model Development & Experiment Management

Reproducible environments (conda/poetry, containers)
Experiment tracking (MLflow/W&B), artifact stores, and registries
PyTorch/TF/sklearn pipelines; hyperparameter tuning; evaluation protocols

Module 4 – CI/CD/CT for ML

Git branching, CI pipelines, model CI (lint, unit tests), data tests, and training pipelines
Continuous Training/Delivery; gates for fairness/bias and safety
Infrastructure as Code (Terraform) and secrets management

Module 5 – Model Serving, Scaling & Performance

REST/gRPC serving (FastAPI, KServe/Seldon), batch jobs, and streaming (Kafka)
Autoscaling on Kubernetes; GPU scheduling; caching
Optimisation: ONNX export, TensorRT, quantisation, distillation

Module 6 – Monitoring, Observability & Reliability

Metrics, logs, traces; Prometheus/Grafana basics
Data/Concept drift detection, feedback loops, retraining triggers
Release strategies: canary, bluegreen, A/B, shadow; rollback and incident response

Module 7 – Governance, Risk, Security & Ethics

AI risk management (NIST AI RMF), ISO/IEC 23894 principles
Model cards, datasheets, approvals, and audit trails
Supplychain security (SBOM, signing), access control, and secure model endpoints

Module 8 – Cost Management & FinOps for ML

Capacity planning, spot/preemptible strategies, and rightsizing
Cost attribution per model/feature; efficiency KPIs
Green AI considerations and sustainability reporting

Awarding Body

Whitestone International College of Innovation

United Kingdom

Qualification Type

Professional Diploma – Postgraduate Level Qualification

(Industry-aligned, regulated qualification issued by Whitestone International College of Innovation, UK)

Assessment & Certification

Assessment: Individual assignments + capstone production demo & defence
Certification: Upon successful completion, participants receive the Professional Diploma in Applied AI Engineering & MLOps from Whitestone International College of Innovation (UK).

Benefits of Attending

UKissued Professional Diploma recognised internationally
Complete toolchain templates: tracking plan, CI/CD/CT pipelines, monitoring dashboards, incident runbooks
Portfolioready artefacts to evidence production competence
Immediate impact on reliability, velocity, and cost of AI delivery

Industry Alignment

Aligned with modern AI/ML engineering practice and guidance: NIST AI RMF, ISO/IEC 23894 (AI risk), ISO/IEC 27001 (security), MLOps lifecycle conventions, and DevOps/SRE best practices.

£ 1250 Per Course

Industry-focused programmes with global standards.
Practical skills for real-world success.
Academic excellence with career-ready outcomes.

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London, united kingdom

+44 20 3727 6493

INfo@whitestoneinternational
college.org.uk