suyogjoshi.com

Software Signal Learning · Stage 5 · In pipeline

Engineering Reliable AI Systems

Engineer a controlled AI workflow that can succeed, abstain, fail safely, fall back, be observed, and recover or roll back with evidence.

This is not a first AI course, prompt course, algorithm course, cloud certification, pure MLOps tutorial, or policy-only governance course.

Designed for: experienced builders who have delivered an integrated ML or GenAI application and bring strong testing, deployment, architecture, and operational foundations.

Planned format
Live engineering cohort
Proposed teaching time
14 sessions × 90 minutes
Expected level
Senior/prior production experience
Applied outcome
Controlled AI workflow
Check your readiness

Starting point

Is this course right for you?

A good fit if you…

  • have built an integrated ML or GenAI application;
  • can test, deploy, and operate software services;
  • reason about architecture, risk, failure, and ownership;
  • want technical controls linked to evidence.

Probably not the right fit if you…

  • have only notebook modelling or chat experimentation;
  • are new to APIs, tests, deployment, or operations;
  • want one cloud certification or tool recipe;
  • want governance without implementation.

Valid entry paths

  • Stage 4
  • Stage 3 plus production software experience
  • Experienced AI/platform engineering direct entry
Check your current readiness

Have you built an ML/GenAI application with real inputs, integration code, evaluation and failure handling—and can you test, deploy, observe, and operate software?

What you will be able to do

  • Define use cases, requirements, risks, and supported boundaries.
  • Select architectures that isolate probabilistic uncertainty.
  • Version data, prompts, models, tools, and configuration.
  • Build evaluation architecture and named release gates.
  • Separate deterministic tests from probabilistic evaluation.
  • Apply security, privacy, and meaningful human oversight.
  • Observe quality, abstention, fallback, latency, and cost.
  • Budget performance and capacity.
  • Use CI/CD and progressive rollout controls.
  • Design incident, fallback, rollback, ownership, and evidence practices.

Capability depends on substantial prior readiness, professional judgement, practice, and completed work; no operational outcome is guaranteed.

Applied outcome

Engineer a controlled AI workflow

Demonstrate not only that an AI system works, but that it can abstain, fail safely, be observed, be controlled, and be recovered—with evidence explaining why it should be trusted.

  1. Succeed
  2. Abstain
  3. Fail safely
  4. Fallback
  5. Observe
  6. Recover
  7. Rollback
  8. Preserve evidence

Deliverables

  • Working service/reference implementation and architecture decision record
  • Requirements, operating boundaries, risk register, and evaluation/release-gate report
  • Security, privacy, oversight, observability, and cost/capacity controls
  • Fallback/rollback, runbook, ownership matrix, evidence chain, and limitations

Boundary: a compact, portable reference stack—not a vendor-certification lab or policy-only document exercise.

How the course is planned to work

Engineer live

Fourteen proposed 90-minute sessions with architecture workshops, reference implementations, threat modelling, and guided controls.

Inject failure

Evaluation, testing, security, oversight, observability, rollout, incident, fallback, and rollback exercises.

Build evidence

Capstone code plus architecture, risk, evaluation, runbook, and ownership artefacts. Stack, review model, support, and recordings remain under validation.

Current curriculum design · Four phases · Fourteen lectures

Detailed curriculum preview

All curriculum is public without registration. Sequence and exercises may be refined before launch without changing the goal or boundary.

Phase 1 — Define the system and its boundaries

Move from a demo to a complete system view, explicit risk, architecture, and traceability.

Lecture 1 — From AI prototype to engineered systemSee a working model call as one system component.

System view/boundaries

  • users, interfaces, logic, data, models, tools, storage, operators; deterministic guarantees versus probabilistic measurement; validation placement

Production concerns

  • availability, security, latency, cost, change management, ownership
Lecture 2 — Requirements, risk, and operating boundariesTranslate a use case into explicit requirements and controls.

Requirements/risk

  • supported/unsupported tasks, thresholds, latency/availability; sensitivity, consequence, blast radius, reversibility, human impact

Boundaries

  • inputs/actions, approvals, budgets, fallback, owners
Lecture 3 — Architecture patterns for AI-enabled systemsIsolate uncertainty and keep systems changeable.

Patterns/components

  • synchronous, async, batch, human queues; model gateway, retrieval, tools, policy, evaluation

Trade-offs

  • coupling, portability, state, failure containment, build/buy
Lecture 4 — Data, prompt, model, and configuration versioningMake behaviour traceable through versioned inputs.

Artefacts/traceability

  • datasets, documents/indexes, prompts, schemas, models, tools, policies; request IDs, snapshots, lineage, reproducible evaluation

Change

  • compatibility, migration, rollout, deprecation, rollback targets

Phase 2 — Build assurance into the system

Establish evaluation, testing, security, privacy, and meaningful human control.

Lecture 5 — Evaluation architecture and release gatesTurn evaluation into repeatable release control.

Layers/data

  • unit, component, end-to-end, human review; golden, edge, adversarial cases; quality, safety, cost, latency

Gates

  • thresholds, regression budgets, waivers, owners, evidence retention
Lecture 6 — Testing deterministic code around probabilistic componentsSeparate exact assertions from statistical evaluation.

Tests

  • parsers, schemas, permissions, routing, tool args, fallback; tolerances, runs, shifts, non-brittle assertions

Environments

  • mocked models, recorded responses, sandbox tools, integration/contract tests
Lecture 7 — Security and privacy by designProtect data, tools, and users across the attack surface.

Threats/controls

  • injection, poisoning awareness, tool abuse, supply chain, isolation; least privilege, secrets, network boundaries, filtering, audit

Privacy

  • minimisation, retention, redaction, consent, vendor boundaries, sensitive output
Lecture 8 — Human oversight and consequential actionsDesign oversight that is meaningful, not ceremonial.

Oversight/approval

  • in/on-loop, post-action, sampling; risk gates, evidence, authority, service levels, escalation

Action safety

  • preview, confirmation, idempotency, reversibility, dual control, kill switch

Phase 3 — Operate, release, and recover

Expose behaviour, control cost, release progressively, and prepare for incidents.

Lecture 9 — Observability for AI behaviourMake quality, failures, and cost visible safely.

Signals/metrics

  • requests, retrieval, responses, tools, validation, overrides; quality proxies, abstention, fallback, latency, token cost, errors

Tracing

  • end-to-end traces, sampling, redaction, dashboards, alerts, diagnostic evidence
Lecture 10 — Operating cost, performance, and capacityControl cost and latency with budgets and architecture.

Cost/optimisation

  • tokens, tiers, retrieval, tools, storage, evaluation; caching, compression, routing, batching, smaller models, async

Capacity

  • rate limits, concurrency, backpressure, queues, load tests, cost-quality trade-offs
Lecture 11 — Deployment, CI/CD, and progressive rolloutRelease changes with automated checks and controlled exposure.

Pipeline/rollout

  • build/test, evaluation gates, security, versioning; flags, shadow, canary, A/B awareness, phased groups

Environments

  • dev/staging/prod, approvals, configuration separation, secrets
Lecture 12 — Failures, fallbacks, incidents, and rollbackPrepare for model, data, vendor, and workflow failure.

Failure/resilience

  • outage, quality, retrieval, tools, cost, unsafe output; timeouts, retries, breakers, fallback, manual mode, queues

Incidents

  • detection, containment, rollback, communication, evidence preservation, learning

Phase 4 — Establish ownership and evidence

Connect controls to accountable ownership and integrate a controlled workflow.

Lecture 13 — Governance, ownership, and evidenceSupport accountability without paperwork theatre.

Ownership/evidence

  • product, model, data, engineering, risk/review roles; intent, context, constraints, alternatives, validation, approval, rollout, monitoring

Lifecycle

  • risk register, change review, reassessment, retirement, third parties
Lecture 14 — Capstone: engineer a controlled AI workflowBuild a service that can be evaluated, observed, constrained, deployed, and rolled back.

Build/controls

  • bounded use case, architecture, model/RAG, action boundary, review; evaluation gate, security, observability, cost, fallback, rollback

Evidence/outcome

  • implementation, ADR, risk register, evaluation report, runbook; succeed, abstain, fail safely, recover with observable evidence

Expected commitment and technical readiness

Proposed teaching

21 instructor-led hours, excluding assignments, project work, and office hours.

Independent work

Architecture/risk exercises, implementation, evaluation data, tests, failure injection, recovery, and operational artefacts. Exact effort will be published.

Technical readiness

Professional engineering judgement and prior AI application evidence. Stack, accounts, cloud/API/tool costs, and environment requirements will be disclosed.

Instructor

Suyog Joshi

More than 20 years in software architecture, enterprise delivery, banking, payments, operational controls, and complex systems.

The course treats AI reliability as an engineering and ownership problem—not only a model-quality problem.

About Suyog

What still needs to be confirmed before launch

The curriculum direction is available for review. Launch operations will be announced only after the following items have been validated.

  • seniority/persona mix and prior AI evidence
  • reference stack, cloud/vendor neutrality, infrastructure and cost
  • failure-injection, security, and privacy lab constraints
  • evaluation, architecture, risk, runbook, and support review capacity
  • cohort size, team exercises, recordings, accessibility, and certificates
  • schedule, fee/tax, payment, application, and selection process

Confirmed dates, fees, delivery conditions and application terms will be published before applications open.

Register interest

Frequently asked questions

Do I need Stage 4 first?

No. Equivalent ML/GenAI application experience is acceptable, but strong software and operational foundations are required.

Can an ML practitioner enter directly?

Yes, with sufficient software, API, testing, deployment, and operations experience. Notebook-only modelling is not enough.

Is this MLOps or governance training?

It overlaps both but is broader: application boundaries, evaluation, security, oversight, cost, incidents, recovery, governance, ownership, and evidence.

Is it tied to one cloud?

No provider is confirmed. The design emphasises portable engineering concepts, not certification.

How are probabilistic components tested?

With evaluation datasets, tolerances, repeated runs, segment checks, non-brittle assertions, and separation from deterministic tests.

What does meaningful oversight mean?

Reviewers receive evidence, hold real authority, and intervene at risk-appropriate points with escalation and recovery paths.

Does registering interest reserve a seat?

No. It creates no application, waitlist, seat, payment, or admission preference.

What counts as prior AI-application experience?

A working application or workflow with real inputs, integration code, evaluation, and failure considerations—not only chat experimentation.

Does it cover cloud deployment?

Deployment concepts, environments, CI/CD, rollout, and rollback are included. This is not a one-cloud product tutorial.

Does it include security and prompt injection?

Yes: injection, tool abuse, data exposure, model supply chain awareness, least privilege, filtering, secrets, isolation, and audit evidence.

What is an evaluation release gate?

A repeatable decision point using defined datasets, metrics, regression budgets, security checks, retained evidence, and named approval ownership.

Will observability and cost be covered?

Yes. Requests, retrieval, output, tools, validation, overrides, latency, token cost, fallback, and quality proxies are included.

What kind of failure injection is used?

Planned scenarios include model outage, degraded quality, retrieval or tool failure, unsafe output, and cost spikes. Exact lab infrastructure remains unconfirmed.

What does the final capstone contain?

A working reference service plus architecture, risk, evaluation, release gate, security, observability, fallback/rollback, runbook, ownership, and evidence artefacts.

Is coding required?

Yes. Architecture and governance are integrated with implementation and operational controls; this is not a no-code management course.

How is this different from Responsible AI and AI Governance?

This course engineers reliable operation of one AI-enabled system. A future governance specialisation may go deeper into organisation-wide classification, fairness, assurance, regulatory readiness, third-party risk, and audit evidence.

Your position in the journey

Stage 4
Generative AI Application DevelopmentAlternate path
Stage 3 + production engineeringStage 5
Engineering Reliable AI Systems

Experienced AI/platform direct entry is also valid. Demand-led directions include LLM and RAG Engineering, AI Agents and Workflow Automation, MLOps and Production ML, and Responsible AI and AI Governance; none is currently scheduled.

View the complete learning journey

Interested in engineering AI systems that can recover?

Record course-specific interest without applying, paying, joining a waitlist, or reserving a seat.

Register interest