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AIOCP · DevOpsSchool Certification

AIOps Certified Professional (AIOCP)

Apply machine learning to IT operations — anomaly detection, event correlation, intelligent alerting, automated incident response and self-healing systems that close the loop. Every session is a live demo in a real lab environment — not slides, not theory. You watch the instructor build it, then you build it yourself.

 4.8 / 5 · 2,300+ ratings 18,000+ certified learners 468 enrolled in last 90 days
Duration
5 weeks
Total content
100+ hours
Per tool
5 hrs · 2 assignments · 1 capstone
Final exam
3 hrs · online · open-book
NEXT COHORT · 1st of next month
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What's included
  • 5-week program · 100+ hours of content
  • Live & interactive instructor sessions
  • 2 assignments & 1 capstone per tool
  • 3-hour online open-book final exam
  • Recordings, slides & lab repos
  • Industry-recognised digital certificate
  • Lifetime forum support — ask anything, forever
  • FREE 1-year LMS access — entire DevOpsSchool LMS: 20+ courses, 50+ tools, videos, quizzes, assignments & projects.
Cohort-cancellation refund. If we cancel or postpone the cohort (instructor unavailability, low enrolment, force majeure), you receive a 100% refund within 15 days. See refund policy.
Reserve my seat — ₹34,999
Engineers we've trained work at
JPMorgan Chase Bank of America Wells Fargo Verizon Nokia World Bank GE Healthcare VMware Oracle Qualcomm Mercedes-Benz Airbus Datadog Splunk Deloitte Infosys Wipro Capgemini
# career outcomes

Walk in an engineer. Walk out a AIOCP who ships.

By the end of AIOCP, you'll have shipped 20 production-grade artefacts and proven you can:

Design CI/CD for multi-service applications, with branching, gates, and progressive rollout.

Provision infrastructure as code across AWS, Azure, or GCP using Terraform — including drift control.

Automate configuration at scale with Ansible — idempotent playbooks, secret-free roles.

Run containers on Kubernetes — workloads, networking, autoscaling, observability.

Shift security left — SAST/DAST, SBOM, signed images, policy as code with OPA.

Operate SLOs — define error budgets, run incident response, write postmortems.

Median salary after certification
$118K – $165K
Roles our AIOCP alumni land: DevOps Engineer · Platform Engineer · SRE · Build & Release Manager · Cloud Automation Lead. Based on alumni reporting, 2024–25.
Start now — ₹34,999
# why this program

It's training built by people who run production for a living.

Taught by senior practitioners

Every instructor has 15+ years operating production systems — our lead instructor, Rajesh Kumar, has 20.

Build your own lab — not a sandbox

We teach you to provision a production-grade environment on your own AWS/Azure/GCP. It's the same skill you'll use day one on the job — and it goes with you when you leave.

100% demo-driven

Every session is a live demonstration in a working lab — never slides, never theory. You watch the instructor build it in real time, then you build it yourself.

Job-ready portfolio

You leave with 20 GitHub-ready projects you can show in interviews tomorrow.

# next cohort

Live cohorts — pick the track that fits your week.

Every cohort is capped at 10 learners by design. That's how the instructor still answers your real production questions in week 4 — not just the rehearsed ones from week 1.

Weekend cohort Most popular

Starts 1st of next month · Sat · Sun · 10:00 AM – 1:00 PM IST
  • 5 weekends · ~8 hrs/weekend live + self-paced
  • Designed for working professionals on IST/EST/GMT
  • Mentor office hours · Sunday 11 AM IST
  • Only 3 of 10 seats left
Reserve seat — ₹34,999

Weekday cohort

Starts 1st of next month · Mon · Wed · Fri · 8:00 – 10:00 PM IST
  • 5 weeks · ~12 hrs/week (live + self-paced)
  • Recorded same-day · always-available replay
  • Mentor office hours · Thursday 7 PM IST
  • Capped at 10 learners — small-batch by design
Reserve seat — ₹34,999

Need a custom corporate cohort for your team? Talk to us →

# curriculum · AIOCP

Tool-by-tool. Live demos, not slides.

Each tool is taught as a working live demonstration inside a real lab environment — you see it built end-to-end before you build it yourself. The structure is identical for every tool, so you always know what's coming and what you'll have shipped by the end of the week.

5 hours
content per tool
(live + self-paced video)
2 assignments
per tool
graded with feedback
1 capstone
per tool
GitHub-public portfolio
3-hr exam
online · open-book
at the end of the program
01 AIOps Fundamentals — AIOps Concepts Video5 hrs · 2 assignments · 1 capstone
The mental model behind everything that follows — what AIOps is, and what it isn't. The Gartner taxonomy. The five AIOps pillars: data ingestion, anomaly detection, event correlation, automated remediation, predictive analytics. Where ML genuinely helps IT ops vs where heuristics win. Maturity model from reactive → proactive → predictive → autonomic. Where the 19 tools that follow fit in.
  • Assignments: (1) score a real (or sample) ITOps org against the AIOps maturity model; (2) for three recent incidents, identify which AIOps pillar would have shortened MTTR — and how
  • Capstone: 12-month AIOps adoption roadmap for a target org — with measurable outcomes (MTTR, alert volume, false-positive rate) and the platform / team investments required
02 Operating System & Scripting — Linux & Bash Scripting Video5 hrs · 2 assignments · 1 capstone
Linux essentials for production telemetry — filesystem, processes, networking, systemd, journald, package managers. The cuts AIOps engineers need: structured-log discipline, syslog formats, eBPF observability primitives, /proc & /sys for host signals, auditd. Bash scripting for ingest-pipeline plumbing.
  • Assignments: (1) shell-script suite that emits journald-friendly structured logs from a sample service; (2) systemd unit + log filter that produces AIOps-ready event signals
  • Capstone: idempotent bootstrap script that takes a vanilla Linux node to a fully-instrumented state — structured logs, host metrics, audit events streaming to your AIOps pipeline
03 Cloud Platform — AWS Live & Interactive5 hrs · 2 assignments · 1 capstone
IAM, VPC, EC2, S3, RDS, EKS, CloudWatch, CloudTrail, EventBridge, Kinesis, OpenSearch Service. The AIOps cuts: CloudWatch Anomaly Detection, DevOps Guru, CloudWatch Logs Insights, EventBridge for routing AIOps-detected events to remediation Lambdas. Cost discipline for telemetry pipelines.
  • Assignments: (1) CloudWatch + DevOps Guru baseline on a sample workload with documented anomaly catch rate; (2) EventBridge rule that routes an anomaly to a remediation Lambda
  • Capstone: end-to-end AWS telemetry + AIOps detection + automated remediation pipeline for a sample 3-service app
04 Cloud Platform — Azure Live & Interactive5 hrs · 2 assignments · 1 capstone
Subscriptions, Entra ID, AKS, Application Gateway, Azure Monitor, Log Analytics, Event Hubs, Logic Apps. AIOps cuts: Application Insights anomaly detection, Smart Detection alerts, Log Analytics KQL anomaly functions, Azure Sentinel ML-based detection, Logic Apps for automated remediation.
  • Assignments: (1) Application Insights Smart Detection on a sample app with measurable signal/noise; (2) Logic App triggered by a Sentinel ML detection that runs a remediation playbook
  • Capstone: Azure-native AIOps pipeline — instrumentation, anomaly detection, automated response — for a multi-service app with measurable MTTR improvement
05 Container Platform — Docker Video5 hrs · 2 assignments · 1 capstone
BuildKit, multi-stage, distroless, image hygiene. The AIOps cuts: HEALTHCHECK semantics, graceful shutdown, structured stdout/stderr, container-level metrics, debugging running containers without restarting. Containerised anomaly-detection workers.
  • Assignments: (1) take a bloated Dockerfile and cut it to distroless multi-stage with HEALTHCHECK + signal handling; (2) containerised log-anomaly-detector that consumes a stream and emits alerts
  • Capstone: production-ready container image template — small, healthy, observable, with metrics + correlation IDs flowing into your AIOps stack
06 Backend Programming — Python Video5 hrs · 2 assignments · 1 capstone
Python the way AIOps engineers need it — virtual envs, packaging (Poetry / uv), click for CLIs, FastAPI for webhooks, pydantic for event schemas, pytest, asyncio for stream consumers. The AIOps patterns: structured logging, correlation IDs, OTel SDK instrumentation, scikit-learn for quick anomaly detection.
  • Assignments: (1) click CLI that ingests a log stream and emits scikit-learn anomaly scores; (2) FastAPI webhook receiving alert payloads with pydantic-validated schemas and OTel tracing
  • Capstone: production-grade Python service for anomaly detection — CLI + ingestion + scoring + webhook + tests + container
07 SCM & DevSecOps — Git, GitHub, GitHub Advanced Security & GitHub Actions Live & Interactive5 hrs · 2 assignments · 1 capstone
Branching strategies, reusable workflows, matrix builds, OIDC to cloud, runner strategies. GHAS (CodeQL, secret scanning, dependency review). AIOps-flavoured: change-volume metrics, DORA tracking, automated change-failure-rate detection, deployment-marker correlation with anomalies.
  • Assignments: (1) reusable workflow that lints, tests, deploys with OIDC and emits deployment-marker events into your AIOps pipeline; (2) GHAS-enabled repo with required-review on prod paths
  • Capstone: production pipeline that auto-correlates deployments with post-deploy anomalies and triggers Argo Rollouts-style auto-rollback when correlation crosses threshold
08 Code Analysis & Security Testing — SonarQube, OWASP Threat Dragon, OWASP Dependency-Check & OWASP ZAP (SAST · DAST · SCA) Live & Interactive5 hrs · 2 assignments · 1 capstone
SAST with SonarQube (quality gates), DAST with OWASP ZAP, SCA with Dependency-Check, threat modeling with Threat Dragon. From an AIOps lens: how each gate affects production incident rate and how to correlate code-quality metrics with operational anomalies.
  • Assignments: (1) SonarQube quality gate tuned for reliability metrics; (2) ZAP scan automated in CI for a public-facing service
  • Capstone: security-testing pipeline plus dashboards correlating SAST findings with incident rate trends
09 Container Orchestration — Kubernetes, Helm & OpenShift Live & Interactive5 hrs · 2 assignments · 1 capstone
Workloads, Services, Ingress, RBAC, HPA / VPA, NetworkPolicies, StorageClasses. Helm & OpenShift Routes / Operators. AIOps-specific: kube-state-metrics + cAdvisor for cluster telemetry, KEDA for event-driven autoscaling, Kyverno / OPA admission events as anomaly inputs.
  • Assignments: (1) Helm chart for a microservice with HPA, KEDA + readiness/liveness probes that don't lie; (2) anomaly-detector deployed as a sidecar / operator that publishes findings into K8s events
  • Capstone: production microservice on OpenShift / EKS with self-healing rules driven by AIOps-detected anomalies
10 Infrastructure as Code — Terraform Live & Interactive5 hrs · 2 assignments · 1 capstone
Modules, state, workspaces, drift detection, import, Terragrunt, Terratest. AIOps-flavoured: Terraform for the AIOps platform itself (Databricks workspaces, Datadog monitors, PagerDuty services), drift detection wired to anomaly pipelines, infra-change events as AIOps inputs.
  • Assignments: (1) Terraform module for a Datadog monitor + PagerDuty service pair, fully parameterised; (2) drift detection wired to publish an event into your AIOps pipeline
  • Capstone: end-to-end AIOps platform as code — multi-env, drift-detected, with CI gating and Terratest coverage
11 Log Monitoring — ELK Stack Video5 hrs · 2 assignments · 1 capstone
Elasticsearch, Logstash, Kibana, Beats. Index patterns, ILM policies, parsing pipelines, query DSL, Kibana dashboards. AIOps cuts: Elastic ML (anomaly detection jobs, classification jobs, outlier detection), Watcher for ML-driven alerts, Lens dashboards with anomalies overlaid.
  • Assignments: (1) Elastic ML anomaly-detection job over a 7-day log baseline with documented signal/noise; (2) Watcher alert that fires on an Elastic ML anomaly score above threshold
  • Capstone: centralised log + Elastic ML stack — ingestion, ILM, dashboards with ML overlays, alerts feeding into your incident-response system
12 Observability & Monitoring — Prometheus, Grafana, OpenTelemetry & Jaeger Live & Interactive5 hrs · 2 assignments · 1 capstone
PromQL, recording & alerting rules, exporters, federation, Thanos / Cortex. OpenTelemetry SDK + Collector for metrics / traces / logs. Jaeger for distributed tracing. AIOps cuts: burn-rate alerts, anomaly detection in PromQL (predict_linear, holt_winters), Grafana ML / Anomaly panels, trace-derived service-graph anomalies.
  • Assignments: (1) instrument a service end-to-end with OTel + multi-window burn-rate alerts; (2) Grafana ML panel surfacing a real anomaly with a documented investigation runbook
  • Capstone: end-to-end observability + AIOps stack — SLOs, anomaly-overlaid dashboards, burn-rate alerts, trace-driven RCA runbooks
13 Data Platform · MLOps · DataOps · GenAI — Databricks Live & Interactive5 hrs · 2 assignments · 1 capstone
The platform module for AIOps workloads. Databricks Lakehouse, Delta Lake, Delta Live Tables, Unity Catalog, MLflow, Model Serving, Vector Search. Why Databricks shines as an AIOps backbone — combining streaming telemetry ingestion, lakehouse storage, and ML model serving in one platform.
  • Assignments: (1) Delta Live Tables pipeline ingesting a real telemetry stream with quality expectations; (2) MLflow-registered anomaly-detection model serving via Databricks Model Serving
  • Capstone: end-to-end Databricks AIOps platform — ingestion, feature engineering, model training, serving, monitoring — for a production telemetry source
14 Observability & AIOps — Datadog & Dynatrace Live & Interactive5 hrs · 2 assignments · 1 capstone
The two leading commercial AIOps platforms, side by side. Datadog: APM, Cloud SIEM, dashboards, SLOs, Watchdog AI (automatic anomaly detection, root-cause hints), Workflow Automation. Dynatrace: Smartscape topology, OneAgent, Davis AI (deterministic causation, problem analytics), automated remediation hooks via Workflows.
  • Assignments: (1) Datadog APM + Watchdog tuned for a real service, with measurable signal/noise; (2) Dynatrace Davis-AI investigation of a production problem with root-cause runbook
  • Capstone: production AIOps monitoring with automated incident detection across Datadog + Dynatrace and an integrated remediation workflow
15 Data Management — Databricks (Unity Catalog) Live & Interactive5 hrs · 2 assignments · 1 capstone
Governing the data behind your AIOps models. Unity Catalog: catalogs, schemas, tables, views, volumes, row/column filters, dynamic views, ABAC, audit logs, lineage end-to-end. Data quality monitoring with Lakehouse Monitoring, freshness SLOs on telemetry streams, schema-evolution discipline.
  • Assignments: (1) Unity Catalog schema with row-level filters + audit logs for a sensitive telemetry table; (2) Lakehouse Monitoring on a streaming telemetry source with drift + freshness alerts
  • Capstone: governed AIOps data layer — catalogs, ACLs, freshness/quality monitoring, lineage queries, audit-ready
16 Experiment Tracking — MLflow & Databricks Live & Interactive5 hrs · 2 assignments · 1 capstone
MLflow as the engine that keeps AIOps models reproducible. mlflow.start_run, autologging for sklearn / TF / PyTorch / LightGBM, parameter / metric / artefact logging, nested runs for sweeps, comparison UI, Unity Catalog Models, alias-based promotion (champion / challenger) for anomaly-detection models.
  • Assignments: (1) instrument an existing anomaly-detection training script with mlflow autolog + custom drift metrics; (2) nested-run hyper-param sweep promoting the best champion via Unity Catalog alias
  • Capstone: a tracked-by-default training workflow for AIOps models — every experiment reproducible, comparable and ready to promote
17 Data Management & Pipeline Orchestration — Databricks (Workflows + DLT) Live & Interactive5 hrs · 2 assignments · 1 capstone
The orchestration layer. Databricks Workflows (multi-task jobs, dependencies, retries, parameter passing, conditional runs), Delta Live Tables for declarative pipelines, Asset Bundles for IaC-style pipeline deployment. Scheduled retraining, online-evaluation jobs, drift-triggered retraining workflows.
  • Assignments: (1) Databricks Workflow that runs a feature-prep → train → eval → register pipeline with retries; (2) drift-triggered retraining workflow gated on data-quality checks
  • Capstone: production-grade AIOps model retraining pipeline — scheduled + drift-triggered, with full lineage and approval gates
18 Incident Management & Automated Response — PagerDuty Video5 hrs · 2 assignments · 1 capstone
The action half of AIOps. PagerDuty: services, escalation policies, on-call schedules, urgency & routing rules. Event Intelligence for alert grouping, AIOps signal de-duplication, Auto-Pause Incident Notifications, Process Automation runbooks triggered by alerts, Status Pages. Healthy on-call: rotation cadence, paging volume budgets, fatigue indicators.
  • Assignments: (1) PagerDuty Event Intelligence configured for an alert source with measurable de-dup ratio; (2) Process Automation runbook that auto-restarts a service on a specific anomaly pattern
  • Capstone: end-to-end PagerDuty setup — alert routing, AIOps de-dup, auto-runbooks for the top 5 incident patterns, postmortem template, paging-volume health dashboard
19 IT Automation & Self-Healing — RunDeck Video5 hrs · 2 assignments · 1 capstone
The remediation loop closer. RunDeck (Process Automation) — jobs, schedules, ACL policies, node resources, key storage, log filters, webhooks. Encoding remediation runbooks as parameterised jobs triggered from AIOps platforms with audit trail and guardrails. Self-service ops for non-admins.
  • Assignments: (1) RunDeck jobs for three common remediation runbooks (restart-service, scale-deployment, drain-node) with ACL boundaries; (2) PagerDuty / Datadog webhook that triggers a RunDeck remediation with payload pass-through
  • Capstone: self-healing platform — 10+ parameterised RunDeck jobs wired to AIOps signals from your observability stack, with audit logs and rollback paths
20 Experimentation with AIOps Platforms — Databricks (Mosaic AI + Bricks for AIOps) Live & Interactive5 hrs · 2 assignments · 1 capstone
The capstone tool. Building custom AIOps models on Databricks when off-the-shelf isn't enough — LSTM / Isolation Forest for anomaly detection on multi-modal telemetry, GenAI-based incident summarisation with Mosaic AI Agent Framework, vector search over historic incidents for similar-pattern retrieval, evaluation harnesses for AIOps model quality.
  • Assignments: (1) Isolation-Forest anomaly model trained on a real telemetry source, deployed via Model Serving, evaluated against labelled incidents; (2) GenAI agent that triages an alert — summarises context, suggests runbook, retrieves similar past incidents via vector search
  • Capstone: a bespoke AIOps experimentation platform built on Databricks — data → training → registry → serving → evaluation against ground-truth incidents
Final certification exam Open-book3 hrs · online · scenario-based
After the 20 tools, you sit a 3-hour online open-book exam. It's scenario-based and tests the full AIOps toolchain end-to-end — designing an anomaly-detection pipeline, debugging an incident with AIOps tooling, authoring a self-healing runbook, drafting an AIOps maturity assessment for an org — not memorisation of flag syntax. See the exam section below.
Want the full module breakdown?

Get the PDF syllabus with every tool, sub-topic, assignment brief, capstone spec and reading list.

Download syllabus
# your capstone portfolio

One capstone per tool. 20 GitHub-public artefacts you'll show in interviews.

Every tool you learn ends in a graded capstone. By the end of AIOCP you'll have a full portfolio of production-grade AIOps work — sample capstones below.

CAPSTONE · TELEMETRY PLATFORM
End-to-end AIOps ingestion pipeline

Multi-source telemetry (logs, metrics, traces, events) into Databricks Lakehouse with quality SLOs and lineage.

DatabricksOpenTelemetryUnity Catalog
CAPSTONE · ANOMALY DETECTION
Isolation-Forest on real telemetry

Trained, MLflow-registered, deployed via Model Serving, evaluated against labelled incidents.

MLflowscikit-learnModel Serving
CAPSTONE · ELASTIC ML
Anomaly + Watcher pipeline

Elastic ML jobs over a 7-day baseline with Watcher alerts that feed PagerDuty Event Intelligence.

Elastic MLWatcherPagerDuty
CAPSTONE · COMMERCIAL AIOPS
Datadog Watchdog + Dynatrace Davis

Both platforms tuned for a real service with measured signal/noise and integrated remediation workflows.

DatadogDynatraceWorkflows
CAPSTONE · AUTO-RESPONSE
PagerDuty Event Intelligence + auto-runbooks

Alert dedup with measurable ratio, auto-trigger RunDeck remediations for top-5 incident patterns.

PagerDutyRunDeckWebhook
CAPSTONE · SELF-HEALING
K8s self-healing via AIOps signals

KEDA + custom operator reacting to AIOps detections — restart, scale, drain, with audit log.

KubernetesKEDARunDeck
CAPSTONE · GENAI TRIAGE
LLM-powered incident triage agent

Mosaic AI Agent that summarises context, retrieves similar past incidents via vector search, suggests runbook.

Mosaic AIVector SearchRAG
CAPSTONE · DRIFT RETRAINING
Workflow-orchestrated retraining

Databricks Workflow that retrains the anomaly model on drift-trigger with full lineage and approval gates.

Databricks WorkflowsDLTMLflow
CAPSTONE · AIOPS AS CODE
Terraform-managed AIOps stack

Datadog monitors + PagerDuty services + Databricks workspace all in Terraform with drift detection.

TerraformDatadogPagerDuty
# the AIOCP toolchain

25+ production-grade observability & AIOps tools, in the order a real AIOps engineer adopts them.

Every tool below is taught as a live demo in a real lab — not slides. You learn how the detection-to-remediation pieces fit, not just what each does.

Linux & Bash
AWS
Azure
Docker
Python
Git
GitHub
GHAS
GitHub Actions
SonarQube
OWASP ZAP
Kubernetes
Helm
OpenShift
Terraform
ELK + Elastic ML
Prometheus
Grafana
OpenTelemetry
Jaeger
Databricks
Datadog Watchdog
Dynatrace Davis
Unity Catalog
MLflow
DLT Workflows
PagerDuty
RunDeck
Mosaic AI
# the final exam

3 hours. Online. Open-book. Built to test what you can ship.

The AIOCP examination is intentionally not a memorisation contest. Open-book, scenario-driven, and proctored online — it tests whether you can solve real production problems with the toolchain you spent five weeks practising.

3 hours
total duration
Online
from anywhere
Open-book
notes, docs, the LMS
Scenario-based
real engineering tasks

What it covers
  • Multi-part production scenarios that span the toolchain end-to-end
  • Pipeline design, IaC, configuration, containers, K8s, observability, security
  • Debugging exercises — given symptoms and logs, find the root cause
  • Written reasoning on trade-offs (e.g. blue/green vs canary, push vs pull GitOps)
Why open-book

In a real on-call shift you look things up. The exam mirrors that. We test the skill that actually matters — composing what you know into a working solution under time pressure. Memorising flag syntax wouldn't make you a better engineer.

Pass → certified.

Clear the exam and you'll be issued the AIOCP digital certificate within 5 working days, with a verifiable credential ID on our public registry.

  • Two free re-attempt windows if you don't clear first time
  • Detailed feedback report on every section
  • Mock papers + walkthrough during the program
  • Hard copy of the certificate on request
See the credential
# meet your instructor

You're not learning from a content team. You're learning from the person who built it.

RK

Rajesh Kumar

Principal DevOps Engineer and Architect
20 years · DevOps · SRE · Security Early-bird practitioner · MLOps · AIOps Ex-PayPay · SoftwareAG · ServiceNow · Adobe · Intuit · IBM · Accenture 10,000+ engineers trained M.Tech · BITS Pilani 25+ certifications

Rajesh is a working practitioner with 20 years across DevOps, SRE and Security, and an early-bird operator in MLOps and AIOps — he was already running model-deployment and telemetry-driven incident pipelines years before either term became industry vocabulary. He has held principal engineering and architect roles at PayPay, SoftwareAG, ServiceNow (Netherlands), JDA Software, Intuit, Adobe, IBM/Emptoris, Ness, MindTree and Accenture. He has personally trained engineers at JPMorgan Chase, Wells Fargo, Bank of America, Verizon, Nokia, World Bank, GE Healthcare, VMware, Citrix, Oracle, Qualcomm, Ericsson, Splunk, New Relic, Datadog, Airbus, AstraZeneca, Bosch, Mercedes-Benz, Vodafone, Deloitte, EY, Capgemini, Infosys, Cognizant, HCL, Wipro and dozens more. He teaches what he runs — not what he reads.

# your credential

A certificate engineers actually recognise — and recruiters look for.

Every AIOCP certificate is issued with a unique credential ID, a tamper-proof QR code, and a verification URL on devopsschool.com/certificates. Add it to LinkedIn in one click.

  •   Lifetime verifiable on our public registry
  •   PDF + digital badge (Credly-compatible)
  •   Recognised by hiring partners across 50+ countries
  •   Hard copy shipped on request — order here
Get certified — ₹34,999
Certificate of completion
Jane Engineer
has successfully completed
AIOps Certified Professional (AIOCP)
Credential ID · DS-AIOCP-XXXX-XXXX
# what learners say

4.8 / 5 from 2,300+ engineers. Here's what a few of them said.

# pricing

Pick the level of support that fits your goal.

Every plan includes the full curriculum, recorded sessions, and access to our learner community.

Every plan includes 1 year of full DevOpsSchool LMS access.
Not just this one course — the entire LMS: 20+ courses, 50+ tools, videos, quizzes, assignments, and end-to-end projects. Worth ₹40,000+ on its own.
See what's in the LMS
Self-paced video ₹833 / month · billed yearly (₹9,996) All recorded sessions, labs & the full LMS — learn at your own pace.
  • Full 100+ hour recorded curriculum
  • 20 hands-on capstones on your own cloud lab (free-tier setup walkthrough included)
  • 1-year access — recordings, labs & updates
  • 3-hr online open-book exam
  • Industry-recognised certificate on completion
  • Lifetime forum support
  • Full LMS access — 20+ courses & 50+ tools
  • Live instructor classes
  • 1-on-1 mentor sessions
Get self-paced — ₹833/mo
1-on-1 Mentorship ₹99,999 full program Dedicated senior practitioner. Pace, schedule and labs tailored to you.
  • Everything in Live & Interactive
  • Private 1-on-1 instructor (your schedule)
  • Custom curriculum & labs for your stack
  • Resume & LinkedIn review
  • Mock interview & salary negotiation prep
  • Capstone & portfolio code review
  • Priority response from instructor
  • Lifetime forum support
  • Full LMS access — 20+ courses & 50+ tools
Enrol 1-on-1 — ₹99,999
Cohort-cancellation refund
If we cancel or postpone a cohort and you decline the rescheduled session, you get 100% refund within 15 days. Refund policy →
Terms & course material
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# why us

Why engineers pick DevOpsSchool over the alternatives.

Not slides. Not a 500-seat MOOC. Not a temporary sandbox login. Three things make the difference — then compare us line-by-line.

100% live demo. 0% slides.

Every session is the instructor screen-sharing a real working lab and building the thing in front of you — then you build it yourself. No PowerPoint, no "imagine if…".

You build your own lab.

We guide you through provisioning a free-tier AWS / Azure / GCP environment on day one — the same skill you'll use at work. A temporary sandbox login disappears the day the cohort ends. Your own lab doesn't.

10 learners. By design.

Cohorts are capped at 10 by design. The instructor still knows your name in week 4 — and still has time to debug the weird production thing you brought from work.

What matters YouTube + blogs Generic online course Boot camp DevOpsSchool AIOCP
Teaching method You piece it together yourself Pre-recorded talking-head + slides Mix of slides & some labs Live demos in a real lab — every session
Cohort size 1 (you, alone) Hundreds to thousands 30–60 per batch 10 by design — instructor knows your name
Lab environment None Throwaway sandbox Shared sandbox login Your own AWS/Azure/GCP, guided setup
Per-tool structure Ad-hoc Inconsistent across modules Theme-based, varies wildly 5 hrs · 2 assignments · 1 capstone for every tool
Final assessment None Multiple-choice quiz Mini-project 3-hour open-book scenario exam
Portfolio at the end What you built solo 1–2 generic toy projects 1 capstone 1 capstone per tool — GitHub-public
Instructor pedigree Mixed (creator-economy) Mixed (often academic) Recent-grad TAs common Rajesh Kumar — 20 yrs, ex-PayPay/ServiceNow/Adobe
Cohort start cadence N/A — pure self-pace Self-paced only Quarterly windows New cohort every 1st of the month
Post-program support None Drip-fed retention emails 30–90 day Slack Lifetime forum + alumni community
LMS bundled No This one course only This program only 1 year full LMS — 20+ courses, 50+ tools
Refund posture N/A Vendor-specific, often none after start Usually none after week 1 100% within 15 days if we cancel
Total cost (full program) Free, slow ₹15K – ₹50K per single course ₹80K – ₹3L+ ₹34,999 · LMS + lifetime forum included

Still on the fence? Talk to an advisor →   — they'll tell you straight if AIOCP fits your goal.

# frequently asked

Everything you'd ask on a 1-on-1 call.

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Do I need prior DevOps or coding experience?
A working knowledge of Linux command line and basic Git is enough. We'll bring you up to speed on everything else from Module 1. About 30% of every cohort enters from a sysadmin / dev / QA background.
What if I miss a live class?
Every session is recorded and shared with the cohort within 24 hours. You retain access to the recordings and lab repositories for the duration of the cohort and a defined access window after it. Specific access duration is confirmed at enrolment.
How does the certificate work? Is it accredited?
We issue a DevOpsSchool-credentialed digital certificate plus a verifiable badge. Each certificate has a unique credential ID and a public verification URL. While it isn't a vendor exam like AWS or CNCF, every cohort includes coaching toward those external exams as a track-add.
Can I pay in instalments / EMI?
Yes — 3, 6, and 12-month plans are available via our payment partners with 0% interest on the 3-month option. We also support employer invoicing.
What's the refund policy?
Once a training cohort is confirmed, the seat is generally non-refundable. The exception is when we cancel or postpone — instructor unavailability, low enrolment, or force majeure — in which case you receive a 100% refund within 15 working days, or you can join the rescheduled cohort. GST and payment-gateway fees are not refunded. Full details on the refund policy page.
Do you give us a cloud sandbox, or do we set one up?
We do it the way you'll do it on the job — you provision your own AWS / Azure / GCP lab, and we walk you through the free-tier setup step-by-step before module 1 starts. Most labs run at zero out-of-pocket. The point is that the skill of owning your infrastructure goes with you forever; a sandbox login disappears the day the cohort ends.
Do you offer corporate or team enrolments?
Yes — private cohorts for teams of 8+ are our most-requested format. We can run them on your schedule, in your VPC, against your internal toolchain. Request a quote.
What time-zones do the live cohorts run in?
Default schedule is IST-friendly, but the weekend cohort works for EST/CET/GMT engineers as well. Recordings cover the rest. We also run a North America-specific cohort every quarter — ask us for the calendar.
Still on the fence?

Talk to an advisor — they'll tell you straight whether this fits your goal.

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Next cohort starts 1st of next month. Only 3 of 10 seats remaining. Drop your details and we'll send the full syllabus + book a free 20-min consult to map this cert to your goal.

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