Private corporate batches, live online cohorts and 1-on-1 mentoring in removing the human release gate safely — feature flags, progressive rollout, automated verification and automatic rollback — taught by a practitioner who runs it in production.
20 years across DevOps, SRE and Security · 10,000+ engineers trained · Trained teams at JPMorgan Chase, Verizon, Nokia and the World Bank
Principal DevOps Engineer & Architect
Rajesh teaches Continuous Deployment around the safety mechanisms rather than the deployment command: decoupling deploy from release with feature flags, progressive rollout through blue-green, rolling and canary strategies, automated analysis against error rate and latency signals, and rollback that triggers without a human in the loop. Sessions cover the preconditions honestly — what test confidence, observability and data compatibility must exist first, and when removing the gate is the wrong decision — plus governance for regulated environments where automatic release still has to produce an audit trail. Twenty years across DevOps, SRE and Security and 10,000+ engineers trained inform the blast-radius and rollback judgement in the harder modules.
Twenty years across DevOps, SRE and Security, in principal and architect roles at PayPay, SoftwareAG, ServiceNow, JDA Software, Intuit, Adobe and others. He has trained engineers at JPMorgan Chase, Verizon, Nokia, the World Bank, VMware, Oracle, Mercedes-Benz and Airbus — more than 10,000 people personally. He teaches what he runs, not what he reads.
You are booked with a named engineer, and that is who turns up. Marketplaces and larger providers rotate whoever is free, so the person who sold you the agenda is rarely the person teaching it.
The same trainer is available for the next engagement, which matters when a team builds on what it learned last time.
Your batch is assigned a named trainer before it starts, and that is who teaches it. See the full faculty.
Engagements are matched on the tool, not the calendar. For Continuous Deployment that means a trainer who has run it in production — removing the human release gate safely — feature flags, progressive rollout, automated verification and automatic rollback — rather than whoever is free that week. You are told who is teaching before you commit, and that person is on the discovery call that shapes the agenda.
Where a batch is large enough to need a second trainer, the pairing is declared up front. The lead trainer stays accountable for the syllabus and the assessment either way.
Principal DevOps Engineer & Architect
Twenty years across DevOps, SRE and Security in principal and architect roles at PayPay, SoftwareAG, ServiceNow, JDA Software, Intuit, Adobe, IBM/Emptoris, Ness, MindTree and Accenture. He has trained more than 10,000 engineers personally, at organisations including JPMorgan Chase, Verizon, Nokia, the World Bank, VMware, Oracle, Mercedes-Benz and Airbus. He teaches what he runs, not what he reads.
Teams of 8–30
Custom agenda, your timezone, onsite or online, NDA-friendly.
Request a quoteIndividual engineers
A private instructor and a curriculum built around your goal.
₹99,999Individuals who want peers
Scheduled batch, max 8 to 10 hours of live instruction.
₹34,999A private batch starts with a discovery call. We look at the stack you actually run — the CI system, the cloud, the constraints — and map the agenda onto it, so examples use your topology rather than a generic one.
Delivery is onsite at your premises, live online, or hybrid, scheduled around your release calendar rather than ours. Batches run 8 to 30 engineers.
Every attendee leaves with recordings, slides, lab repositories and a completion certificate. You receive an attendance and assessment report. Invoicing supports PO and GST.
Talk to us about a private Continuous Deployment batchContinuous Deployment is the practice of automatically releasing to production every change that passes the pipeline, with no human approval step in between. It is one step beyond continuous delivery: where continuous delivery guarantees that a change could be released and leaves the timing to a person, Continuous Deployment removes the person. A merge to mainline that goes green ends up serving live traffic, usually within minutes, without anyone deciding it should.
That only works if the confidence normally supplied by a human reviewer is supplied by something else. In practice it is supplied by four things: a test and verification suite trustworthy enough that a green result means something; the ability to release a change to a small fraction of traffic first; automated analysis that compares that fraction's error rate, latency and business signals against a control; and rollback that fires automatically when the comparison fails. Blue-green, rolling and canary deployments are the mechanics; automated canary analysis and bake periods are the judgement.
The other half of Continuous Deployment is the separation of deployment from release. Code reaching production and a feature being visible to users become independent events, controlled by feature flags. Incomplete work ships behind a flag that is off; a finished feature is exposed to one percent of users, then ten, then everyone, on a schedule the team controls and can reverse in seconds. That decoupling is what makes automatic deployment tolerable to a business that still needs control over what customers see, and managing the resulting flag inventory is a real engineering responsibility rather than an afterthought.
The economics are straightforward and well evidenced: smaller changes fail less often and are far easier to diagnose when they do. A deployment containing a single commit has one suspect. Continuous Deployment takes that argument to its conclusion by making the batch size exactly one change, and it forces an organisation to build the safety mechanisms that make small, frequent production change survivable.
What has changed recently is that the tooling to do this responsibly is now ordinary. Traffic splitting through a service mesh or ingress controller, progressive rollout controllers driven by declarative manifests, automated analysis against metric queries, and flag platforms with audit trails are all commodity capability. The barrier is no longer the technology; it is knowing which preconditions must be true before removing the gate, and having the observability to detect a bad release automatically rather than by customer complaint.
That makes it a specific and well-paid skill. Organisations do not need someone who can deploy — they need someone who can decide what must be true before automatic deployment is responsible, build the rollout and rollback machinery, define the analysis that decides promote-or-abort, and explain to a risk function why an automated pipeline with progressive rollout is safer than a monthly release approved by a committee.
What the human approval step is actually providing, and what has to replace it. The preconditions — test confidence, observability, reversibility, small changes, data compatibility — assessed as a checklist, plus an honest account of the services and industries where automatic deployment is the wrong answer and continuous delivery is the correct stopping point.
Topics: Continuous Deployment versus Continuous Delivery: the one difference · What a human approver actually contributes, and what they do not · Preconditions: test confidence, observability, reversibility, batch size · Blast radius and consequence classification per service · Services where automatic deployment should not be used · Getting organisational agreement to remove the gate · Readiness assessment as a repeatable review
The mechanism that makes automatic deployment acceptable to a business. Shipping unfinished work behind flags that are off, exposing features to cohorts independently of deployment, dark launching to exercise new code paths with real traffic, and the flag lifecycle — because a flag that is never removed becomes a permanent, untested branch in the code.
Topics: Release toggles, ops toggles, experiment toggles and permission toggles · Shipping incomplete work safely behind an off flag · Targeting by user, cohort, tenant or percentage · Dark launching and shadow traffic · Kill switches for operational control · Flag lifecycle: naming, ownership, expiry and removal · Testing with flags: combinatorial explosion and how to limit it
The mechanics of putting a new version in front of users gradually. Recreate, rolling, blue-green and canary compared on cost, risk and rollback speed; traffic shifting by weight, header and cohort; and the session, connection and cache behaviour that makes real rollouts messier than the diagrams.
Topics: Recreate, rolling, blue-green and canary compared · Choosing a strategy from blast radius and cost · Traffic splitting with ingress controllers and service meshes · Weighted routing, header-based routing and sticky sessions · Kubernetes rollouts, readiness and liveness probes · Draining connections and handling in-flight requests · Multi-region and multi-cluster rollout sequencing
The module that replaces the human judgement. Defining the signals a canary is judged on, comparing against a control rather than an absolute threshold, choosing a bake period long enough to be meaningful, and the statistical traps — low traffic, noisy metrics and warm-up effects — that produce both false promotions and false aborts.
Topics: Choosing analysis signals: error rate, latency, saturation, business metrics · Baseline versus canary comparison and why absolutes mislead · Automated canary analysis with Argo Rollouts, Flagger or Kayenta · Bake time, sample size and low-traffic services · Warm-up effects, JIT and cold caches skewing early data · Abort criteria and progressive promotion steps · Smoke and synthetic checks as an entry gate to analysis
Reversal as a designed capability rather than a hope. Automatic rollback triggers, the cases where rolling forward is safer, and the constraint that governs everything: you can revert code in seconds, but you cannot revert data. Backward-compatible schema and API change, and limiting the damage a bad release can do before it is caught.
Topics: Automatic rollback triggers and their relationship to analysis · Rollback versus roll-forward and how to decide under pressure · Backward-compatible database change and expand-contract · API and message schema versioning for reversibility · Irreversible operations and how to isolate them · Limiting blast radius by cohort, region and tenant · Rehearsing rollback so it is not first attempted during an incident
Automatic deployment is only as good as the signal that says something is wrong. Instrumentation that makes a bad release visible within a rollout window, alerting tuned for deployment-related failures, and the error budget as the brake — an automated policy that slows or halts deployment when reliability is already spent.
Topics: Instrumenting a service so a bad release is detectable in minutes · Deployment markers, change correlation and attribution · Service level objectives as the deployment quality bar · Error budgets as an automated deployment brake · Alerting for release-induced regressions specifically · Production readiness review before enabling automation · Post-deployment verification and synthetic monitoring
How an automated pipeline satisfies audit, change management and segregation of duties when nobody clicks approve. GitOps as a control plane with the repository as the record, generating change records from deployment events, and the argument to make to a risk function about why small automated releases carry less risk than large approved ones.
Topics: GitOps as the deployment control plane and system of record · Argo CD and Flux: desired state, drift detection and reconciliation · Segregation of duties enforced by repository permissions and review · Generating change records automatically from deployment events · Emergency changes and break-glass procedures · Pre-authorised standard changes for pipeline deployments · Presenting the risk case to audit, security and change management
We rebuild the agenda around the tools you actually run.
Request a custom agendaShip an unfinished feature to production behind an off flag, expose it to a single test cohort, roll it out by percentage and then kill it instantly — all without a deployment.
Implement both strategies for the same service, deploy a broken version through each, and record detection time, user impact and rollback duration side by side.
Configure automated canary analysis with real metric queries, bake time and abort criteria, then deploy one healthy and one degraded build and confirm the pipeline judges each correctly.
Ship a schema change alongside code using expand and contract, then roll the code back automatically while the data stays consistent and the service stays up.
Drive deployments entirely through Git with a reconciliation controller, break the cluster state by hand, and watch drift detection restore it — then produce the audit trail.
Deliver a service where a merge reaches production automatically through progressive rollout with automated analysis, and demonstrate it with one good release and one that aborts itself.
Billed yearly at ₹9,996
Max 8 to 10 hours of live instruction
Full program, private instructor
8–30 engineers · custom agenda · onsite or online · PO and GST invoicing
Refunds. If we cancel or postpone a cohort, you get a full refund within 15 days. There is no money-back guarantee otherwise.
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Certificate of completion
Helped to understand more on overall DevOps concepts.
Got good lab sessions which kept the new DevOps tool learnings to the point and it helped a lot in my career.
The trainer (Rajesh) provided very good sessions on SRE profession. Not only hands-on learning on the tools but also SRE mindset.
Very good training session. Well explained from the basics to the complex concepts. Also tried to cover practicals and demos within the 3 hour sessions. The learning content and videos are of a great deal of help.
Basics explanation was exemplary from Rajesh where he dealt with complicated topics to be simple. Great learning stuff personally for me.
Very detailed explanation and has lots of patience in attending the questionnaire. Thanks again for your wonderful sessions.
| What matters | YouTube + blogs | Generic online course | Freelance marketplace | DevOpsSchool |
|---|---|---|---|---|
| Named practitioner | No | Rarely | Varies per booking | Yes — same trainer each time |
| Production experience | Unknown | Unknown | Unverified | 20 years, named employers |
| Custom agenda | No | No | Sometimes | Built from your stack |
| Onsite delivery | No | No | Sometimes | Yes |
| Lab environment | None | Sandbox that expires | Varies | Your own cloud — skill goes with you |
| Assessment | None | Quiz | Rarely | Assignments + capstone per module |
| Per-attendee certificates | No | Sometimes | Rarely | Yes |
| Corporate invoicing | No | Limited | Varies | PO and GST |
| Post-training support | None | Forum, time-limited | None | Lifetime forum access |
Tell us the team, the stack and the timeline. You'll get a straight answer, not a sales sequence.
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