Operationalise machine learning end-to-end — from notebook to feature store to model serving — with the observability, governance and reproducibility that let ML earn real budget. 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.
By the end of MLOCP, you'll have shipped 22 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.
Every instructor has 15+ years operating production systems — our lead instructor, Rajesh Kumar, has 20.
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.
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.
You leave with 22 GitHub-ready projects you can show in interviews tomorrow.
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.
Need a custom corporate cohort for your team? Talk to us →
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.
Get the PDF syllabus with every tool, sub-topic, assignment brief, capstone spec and reading list.
Download syllabusEvery tool you learn ends in a graded capstone. By the end of MLOCP you'll have a full portfolio of production-grade MLOps work — sample capstones below.
Streaming + batch feature pipeline with data-quality expectations, lineage, freshness SLOs.
PyTorch DDP / TF MirroredStrategy on 2 GPUs, checkpointed, MLflow autolog, TensorBoard / TB-X.
sklearn validation harness, CheckList-style behavioural tests, model-quality regression gate in CI.
MLflow-logged model with signature + input example + pinned env, Unity Catalog promotion workflow with approval webhook.
Real-time endpoint with scale-to-zero, A/B champion/challenger split, request logging, auto-rollback on metric regression.
KServe deployment with GPU scheduling, HPA, NetworkPolicies, full observability wiring — alternative to managed serving.
OTel instrumentation, Grafana SLO dashboards, prediction-distribution drift indicators, burn-rate alerts.
Production monitoring for a model service with auto-detection of drift, regression and latency anomalies, mapped to on-call.
Multi-env Terraform-managed Databricks + Azure ML workspaces with cluster policies, drift detection, Terratest.
Every tool below is taught as a live demo in a real lab — not slides. You learn how the ML lifecycle pieces fit, not just what each does.
The MLOCP 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.
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.
Clear the exam and you'll be issued the MLOCP digital certificate within 5 working days, with a verifiable credential ID on our public registry.
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.
Every MLOCP 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.
Every plan includes the full curriculum, recorded sessions, and access to our learner community.
Need an invoice for your employer? Request a corporate quote → · Taxes (GST) where applicable are billed in addition to the listed price.
Not slides. Not a 500-seat MOOC. Not a temporary sandbox login. Three things make the difference — then compare us line-by-line.
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…".
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.
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 MLOCP |
|---|---|---|---|---|
| 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 MLOCP fits your goal.
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Talk to an advisor — they'll tell you straight whether this fits your goal.
Talk to advisorNext 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.