Deploy useful, secure AI inside complex organisations—connecting models, data, systems, and people in production.
This programme develops engineers who can move from ambiguous enterprise needs to secure, evaluated, production-ready AI systems.
Each block combines explanation, guided implementation, review, and a practical delivery artefact. Learners translate an uncertain business problem into a measurable AI workflow, test its quality and safety, and explain the operational trade-offs to technical and executive stakeholders.
The programme is for technical professionals who want to deliver applied AI inside real enterprise constraints.
| Learner profile | Why this programme fits |
|---|---|
| Backend and full-stack engineers | Extend production engineering skills into model APIs, orchestration, retrieval, and AI-enabled workflows. |
| Data and analytics engineers | Connect data quality, pipelines, embeddings, evaluation datasets, and reliable enterprise retrieval. |
| DevOps, platform, and MLOps engineers | Build containerised serving, observability, deployment, scaling, and rollback practices for AI systems. |
| Solutions engineers and technical consultants | Translate executive goals and technical constraints into scoped, testable AI delivery plans. |
| Security and infrastructure practitioners | Design privacy boundaries, access controls, model-risk controls, and secure deployment patterns. |
| Applied AI developers and career switchers | Build a portfolio demonstrating an evaluated, governed, production-oriented AI system. |
The 16-week programme combines live instruction, guided implementation, independent practice, evaluation work, and a production capstone.
Plan for approximately 10–14 committed hours per week across live sessions, guided labs, implementation work, reading, and assessment.
Apply the complete FDE loop to a secure enterprise knowledge system, including evaluation, privacy review, monitoring, and an executive presentation.
| Requirement | Minimum / recommended setup |
|---|---|
| Computer | Modern 64-bit computer with at least 16 GB RAM, reliable SSD storage, and permission to install development tools. |
| Development environment | Python 3.11+, Git, Docker or an approved container runtime, a modern browser, and an approved code editor. |
| AI environment | Access to the course environment or approved local/cloud resources for APIs, vector search, evaluation, and model-serving exercises. |
| Connectivity | Stable internet access, webcam, and microphone for live online sessions and collaboration. |
| Working access | Ability to run containers, inspect logs, use APIs, and work with synthetic or approved enterprise-style documents. |
The sequence moves from consultative foundations through applied AI engineering and into an end-to-end production simulation.
| Week | Focus | Detailed topics | Practical output |
|---|---|---|---|
| 1 | FDE mindset and discovery | Embedded delivery, ambiguity, stakeholder discovery, business outcomes, constraints, risk, and success measures. | Discovery plan and stakeholder map. |
| 2 | AI system architecture | Model, data, application, and control planes; latency, reliability, cost, privacy, and architecture trade-offs. | Current-state and target-state architecture. |
| 3 | Requirements and evaluation | Use cases, acceptance criteria, risk tiers, evaluation datasets, quality thresholds, human review, and scope control. | AI product brief and evaluation plan. |
| 4 | Enterprise data pipelines | Ingestion, cleaning, identity, metadata, schema drift, event-driven sync, APIs, retries, and lineage. | Data flow and integration contract. |
| 5 | Embeddings and vector search | Embedding models, cosine similarity, vector stores, HNSW versus IVF, metadata filters, and retrieval metrics. | Indexed document collection and retrieval baseline. |
| 6 | RAG and document intelligence | Chunking, layout-aware parsing, query rewriting, hybrid search, reranking, citations, and context limits. | Measured RAG pipeline with test cases. |
| 7 | LLM orchestration and prompting | Prompt contracts, structured outputs, temperature, tool calling, agent boundaries, max iterations, and failure handling. | Controlled workflow with tool schema. |
| 8 | Guardrails and privacy | Prompt injection, input/output guardrails, PII handling, access controls, data residency, and human escalation. | Threat model and guardrail test pack. |
| 9 | Model serving and containers | Docker, Kubernetes patterns, GPU and CPU constraints, quantisation, vLLM, throughput, latency, and capacity. | Containerised serving design and benchmark. |
| 10 | Observability and MLOps | Logs, traces, cost, token usage, model registry, data lineage, drift, feedback loops, and incident signals. | Monitoring dashboard specification. |
| 11 | Reliability and deployment | CI/CD, infrastructure as code, blue-green releases, rollback, secrets, backups, and disaster recovery. | Release pipeline and recovery runbook. |
| 12 | Enterprise integration | Live data tools, function calling, authentication, service boundaries, rate limits, queues, and secure system access. | Secure tool integration prototype. |
| 13 | Capstone system build | Implement the multi-source knowledge system, connect ingestion and retrieval, resolve defects, and document decisions. | Integrated AI solution increment. |
| 14 | Evaluation and hardening | Faithfulness, recall, precision, safety, cost, latency, adversarial tests, drift baselines, and remediation. | Evaluation report and hardening backlog. |
| 15 | Client simulation | Scope changes, incident response, executive narrative, security review, user adoption, handover, and operational readiness. | Handover pack and executive briefing. |
| 16 | Capstone assessment | End-to-end demonstration, technical review, business-value explanation, peer feedback, portfolio refinement, and development plan. | Final capstone, report, and presentation. |
Assessment focuses on architecture, code resilience, retrieval and model quality, data accuracy, security, documentation, and the clarity of the final client presentation.
Weekly implementation work, container and integration exercises, evaluation evidence, and secure AI engineering decisions.
Discovery notes, scope decisions, risk handling, stakeholder communication, and clear mapping from system behaviour to business value.
A production-oriented AI system, evaluation report, security controls, documentation, and an executive-ready technical presentation.
Learners who meet the attendance, participation, assessment, and capstone requirements will earn a Certificate of Completion digital badge and certificate from Digital Futures Academy. This records successful completion of the programme and is not represented as a government licence or external professional certification.
It is for engineers and technical professionals who want to deliver applied AI with enterprise data, infrastructure, security, and business teams.
You need Python, API, containerisation, and systems fundamentals plus a baseline understanding of machine learning. The focus is applied production delivery rather than research.
It covers online instruction, guided practical work, feedback, assessment activities, and the production capstone. Personal equipment and connectivity remain the learner’s responsibility.
You will deploy a secure internal knowledge-retrieval system over enterprise-style documentation, with evaluation, privacy, monitoring, and executive communication.
Digital Futures may invite applicants to complete the AI FDE assessment to understand technical readiness and delivery judgement.
Ask about cohort dates, fit, and assessment next steps.