Ship modern applications and AI systems — then keep them reliable, secure, observable, governed and affordable.
Move from Linux, Git and three-tier application delivery to Docker, Kubernetes, Terraform, GitHub Actions, GitOps, AWS, Azure and Site Reliability Engineering. Then learn to operate the new assets that change AI behaviour: models, prompts, RAG indexes, evaluation datasets, agent tools, permissions, traces and autonomy controls. Build one connected production portfolio — not a collection of disconnected tool demos.
DevOps & AI Operations is the engineering discipline for reliably building, releasing and operating both software applications and AI systems. Traditional DevOps manages code, infrastructure, pipelines and runtime services. AI Operations adds models, prompts, retrieval indexes, evaluations, agent tools, permissions, trajectories and AI cost as governed production assets.
A demo only needs to work once. A production system must continue working when traffic increases, a dependency fails, a rollout introduces a regression, a cloud credential expires, a retrieval index becomes stale, a model provider slows down, an agent chooses the wrong tool or token spend exceeds the business value created.
The 2025 DORA research treats AI as an amplifier of the surrounding delivery system: local gains do not automatically become organisational performance when testing, deployment, feedback and platform capabilities remain bottlenecks. This course therefore teaches AI-assisted delivery together with the engineering controls that protect throughput, stability, security and recovery.
The value of a system is not proven when it runs once. It is proven when it can be released, operated, recovered and improved repeatedly.
Everything that changes AI behaviour is a release.
Learn how DevOps changes the delivery lifecycle, operate Linux systems, troubleshoot networks and make Git and GitHub the source of truth for collaborative change.
Build and containerise the reference application, operate Kubernetes, provision infrastructure with Terraform and implement secure CI/CD and GitOps.
Build equivalent production fluency across AWS and Azure: identity, networking, compute, storage, databases, managed Kubernetes, monitoring, governance and cost.
Instrument the service, define SLOs and error budgets, run an incident simulation and design a minimum viable internal platform with a self-service golden path.
Version and promote AI assets, enforce evaluation gates, operate model-serving and RAG systems, control agent tools and trajectories, and implement approval-gated AIOps remediation.
Architecture decisions, release evidence, reliability objectives, security controls, AI quality thresholds, agent permission boundaries, incident-response decisions, cost-per-outcome economics, and rollback and kill-switch readiness.
Twelve modules. One production evidence chain.
Understand why DevOps exists before learning its tools. Build the operating-system and networking fluency needed to deploy and troubleshoot real services.
Make version control the system of truth for application code, infrastructure, configuration, policies and AI assets.
Understand the application before automating it. Build, analyse, package and run a complete three-tier service consistently.
Move from individual containers to a declarative, self-healing application platform — and learn the operational responsibilities hidden behind Kubernetes YAML.
Replace manual infrastructure and deployment work with reproducible, reviewable and secure delivery automation.
Develop broad AWS operations capability and apply it to a production-shaped Kubernetes workload.
Build equivalent operations fluency across Microsoft Azure and understand where platform terminology and implementation differ from AWS.
Move from reactive infrastructure monitoring to service reliability, actionable feedback, incident learning, self-service delivery and value-aware operations.
Operate every asset that can change an AI system’s behaviour and make each production response reproducible enough to investigate.
Make quality, latency, safety and cost first-class release criteria for probabilistic AI systems.
Control stateful systems that use tools, affect business systems and may act across multiple steps or agents.
Use AI to improve technology operations without removing human accountability — then integrate the entire course into one defended production system.
Vendor interfaces will change. The durable capability is understanding the operating problem, choosing the right control and proving the outcome. Product names are representative lab choices — a dated lab-stack note is published per cohort.
Take a three-tier learning platform from repository to a governed cloud-native service. Add an AI support capability and a tool-using operations agent. Build the release, observability, approval, incident and cost controls that make the system safe to operate.
Provision a server, deploy Nginx, configure secure access and troubleshoot planted failures.
Build, analyse and package the frontend, backend and PostgreSQL environment with optimised images and Compose.
Deploy the LMS declaratively, automate promotion and demonstrate rollout, drift detection and rollback.
Deploy to a production-shaped AWS or Azure environment, instrument the service and complete an incident simulation.
Version a RAG system, compare two configurations, enforce evaluation thresholds and measure cost per successful task.
Trace a tool-using agent, require approval for consequential actions, replay a failure and execute a governed remediation workflow.
Each phase closes with a certificate; the full credential is awarded on the capstone, defended portfolio and viva.
The course builds role-aligned capability and portfolio evidence. It does not guarantee a job title, seniority, placement, salary or certification pass — entry level depends on prior experience, technical performance and the quality of your defended portfolio.
Seven tracks each own a slice of the enterprise AI value chain. Each is individually employable and individually incomplete — enterprises don't fail at AI because a discipline is missing, they fail at the seams between them. The FDE is the single named human accountable for the whole outcome.
DevOps & AI Operations builds the release, cloud, reliability and governance half of the FDE operating model. A learner who also develops application, data, AI and customer-discovery capability can progress toward owning the complete customer outcome.
This is a technical, command-line-heavy course. Prior professional experience is not mandatory on the foundation route — willingness to practise is.
The titles differ by employer — the role cluster above covers them. What does not differ is the ask: someone who can put a system into production, see what it is doing, and get it back when it goes wrong.
Internal platform groups owning the paved road other engineers ship on, from pipelines through production guardrails.
Teams running model serving, retrieval and agents in production, where cost and latency are operational problems every day.
Practices running client environments under contract, where observability and handover quality are the deliverable.
Regulated production where change control, evidence and rollback are audited on every release.
Multi-tenant platforms where one bad rollout reaches every customer at once and staging is not optional.
Groups on the hook for agents in production: tool permissions, trajectory limits, evaluation gates and the kill switch.
Categories reflect how these roles are commonly scoped and advertised across the market. Availability, titles and requirements differ by employer, region and year, and many roles expect prior industry experience — see the evidence methodology.
Individual outcomes reflect each graduate's prior experience and market conditions. The programme does not guarantee a role — see the evidence methodology.
Enterprise scale is where AI agents earn their keep — an agent plugged into the real service path, orchestrating work across teams as one fabric. That is the bar I teach to, every class.
Manikanta is the founder of KDigital and brings 15 years of enterprise platform architecture, where he led rollouts for Fortune-500 banks, telcos and insurers. Most recently he architected production agent deployments that replaced traditional triage tiers with governed, autonomous case handling.
His deployment modules give you two things other programmes do not: a founding architect who has shipped enterprise AI from inside the Fortune 500, and a curriculum rewritten every release — so when interviewers ask about promotion gates, autonomy ladders or kill switches, you have already wired them.
Operations is where AI stops being a demo and starts being the nervous system of the enterprise — telemetry you trust, alerting that is quiet on purpose, and agents resolving incidents before on-call gets paged.
Ravi is Chief Technologist at KDigital, where he leads the operations-engineering practice. After eight years building and running production DevOps pipelines, he stepped into the Chief Technologist seat to wire observability, evaluation and incident response into the way operations teams actually work.
His operations modules are built from real outage post-mortems, not slide decks. Expect to leave with working monitoring patterns, alert rule packs, cost controls and a release process you can stake an SLA on.
This page deliberately carries no placement percentages, salary figures or borrowed employer logos. Judge the programme on what you can inspect today — and ask us for anything on this list.
"Production engineering is not the ability to run a command. It is the ability to make change repeatable, failure visible, recovery safe and ownership clear."
If the answer you need isn't here, book a 20-minute advisor call. No slides, no pitch — just your questions.
Campuses in Hyderabad and Bengaluru, plus live online cohorts running on Indian and US timezones.
Learn AI. Ship Agents. Keep Them Running.
Book a one-to-one pathway conversation. We'll review your current experience, recommend the foundation or accelerated route and show you the exact production artifacts you'll build.
Adjacent tracks that share modules, faculty and the same evidence standard.
Run AI in production — deployment, observability, cost control, evaluation gates and incident response.
Level: Intermediate to advanced. Delivery: Online, Blended, Onsite. Related roles: DevOps Engineer, LLMOps Engineer, Site Reliability Engineer. Credential: KDigital Academy programme completion credential, awarded on defended portfolio evidence.
DevOps manages the path from code and infrastructure to a reliable running service. AI Operations adds the assets and risks that change AI behaviour: models, prompts, retrieval indexes, evaluation datasets, agent tools, permissions, trajectories and AI cost. This course teaches both as one production system.
Yes, through the foundation route, but this is not a no-code course. You will use a Linux terminal, Git, YAML, cloud consoles, logs and configuration. Learners who are new to these areas should complete the beginner bridge and plan for regular practice.
Yes. The curriculum builds broad operations fluency across both clouds, including identity, networking, compute, storage, databases, monitoring, cost and managed Kubernetes. The exact depth of live cloud labs depends on the approved cohort sandbox.