Private corporate batches, live online cohorts and 1-on-1 mentoring in risk-based automated testing at every pipeline stage, plus test data, environments and testing in production — 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 Testing as pipeline design rather than tool training: which check belongs at which stage, contract testing to displace brittle end-to-end suites, test data and ephemeral environments so tests stop contending for a shared system, and the flakiness and runtime discipline that keeps a suite trusted as it grows. Sessions extend past deployment into synthetic monitoring, canary analysis and chaos experiments, and cover security testing — dependency scanning, SAST and DAST — as part of the same pipeline budget. Twenty years across DevOps, SRE and Security and 10,000+ engineers trained inform the risk-based judgement on what to test, where, and what to stop testing.
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 Testing that means a trainer who has run it in production — risk-based automated testing at every pipeline stage, plus test data, environments and testing in production — 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 Testing batchContinuous Testing is the practice of evaluating the quality and risk of a change automatically at every stage of the delivery pipeline, so that a release decision can be made from evidence rather than from a testing phase at the end. It replaces the model where development finishes and testing begins with one where every commit is assessed continuously — cheaply and quickly at first, then more expensively and more realistically as the change gets closer to production.
The organising idea is risk coverage, not test count. Each stage of a pipeline asks a different question: does this unit behave correctly, do these services still agree on their contract, does the assembled system satisfy its acceptance criteria, does it hold up under expected load, is it free of known vulnerable dependencies. Placing each check at the earliest stage where it can give a trustworthy answer is the core design skill, because a test that only runs in a full end-to-end environment costs a hundred times more per execution than the same assurance obtained from a contract test.
Continuous Testing therefore covers more than automation frameworks. It includes test data that can be created and reset on demand, environments provisioned per branch rather than shared and contended, service virtualisation for dependencies you cannot call freely, and the discipline of keeping a suite fast and non-flaky as it grows. It also extends past deployment: synthetic monitoring, canary analysis and controlled chaos experiments are testing activities that happen in production, and treating them as part of the same strategy is what distinguishes the practice from simply owning a lot of automated tests.
The delivery half of the industry got fast and the assurance half largely did not. Teams that can build and deploy in minutes still schedule a two-week regression cycle, still run a suite that takes six hours, and still carry a list of tests everyone knows fails intermittently. The result is that testing becomes the constraint — and the usual response, writing more end-to-end tests, makes the constraint worse rather than better.
That is a design problem with known solutions, and it is what the market is now hiring for. Job specifications ask for quality engineers rather than manual testers: people who can decide what belongs at which layer, build contract tests that remove the need for brittle end-to-end coverage, manage test data and ephemeral environments, and keep execution time inside a pipeline budget as the codebase grows.
There is also a security and compliance dimension pulling in the same direction. Dependency scanning, static and dynamic application security testing, and licence checks are now expected to run on every change rather than annually, which puts them inside the same pipeline and under the same constraints as functional testing. Whoever designs the test strategy increasingly designs the security assurance strategy too.
Why a testing phase cannot survive a fast pipeline, and what replaces it. Risk-based coverage as the organising principle, mapping each risk to the cheapest stage that can address it, and the economics that make a big end-to-end suite a false economy. Includes what to stop doing, which is usually the harder half.
Topics: From a test phase to continuous evaluation of risk · Risk-based coverage: identifying what actually needs assurance · Cost per execution by test layer and why it dominates design · Quality gates and what constitutes a release verdict · Whole-team ownership of quality · Deciding what not to test, and retiring existing tests · Testing quadrants as a coverage checklist
The pyramid as an argument about feedback cost, and how to apply it to an existing inverted suite. Unit, component, integration, contract, end-to-end and exploratory testing — what each is genuinely good at, where each belongs in the pipeline, and how to rebalance a suite without a rewrite.
Topics: Test pyramid, testing trophy and honeycomb models compared · Unit and component tests: scope and isolation boundaries · Integration tests against real dependencies and their cost · Contract testing with Pact and consumer-driven expectations · End-to-end testing: the minimum viable set · Exploratory testing as a deliberate, chartered activity · Rebalancing an inverted pyramid incrementally
Writing automated tests that survive contact with a changing product. Framework structure, page objects and screenplay patterns for UI tests, API-level testing as the preferred default, and the mechanics of running them in CI — parallelisation, sharding, containerised browsers and reports someone will actually read.
Topics: UI automation with Selenium, Playwright and Cypress · Page object and screenplay patterns for maintainable UI tests · API testing with REST clients, Postman and code-level frameworks · Test frameworks: JUnit, TestNG, pytest, and structure conventions · Running suites in CI: parallelisation, sharding and containers · Reporting, failure triage and actionable output · Cross-browser and mobile execution strategy
The infrastructure problem that limits most test suites. Creating and resetting test data on demand instead of depending on a shared dataset someone corrupted, provisioning ephemeral environments per branch, and virtualising or stubbing the third-party dependencies you cannot call on every build.
Topics: Test data strategy: synthetic generation, subsetting and masking · Data isolation, seeding and reset between test runs · Privacy and regulatory constraints on production-derived data · Ephemeral environments per branch or pull request · Containerised dependencies with Testcontainers · Service virtualisation and stubbing for third-party systems · Managing contention on shared environments
Performance, security, accessibility and resilience treated as continuous checks rather than annual events. Load profiles that reflect real traffic, thresholds you would actually act on, and security testing — dependency scanning, SAST, DAST — placed where it informs a decision without stopping every build for noise.
Topics: Performance testing with JMeter, k6 and Gatling · Load, stress, soak and spike profiles and when each applies · Performance baselines, thresholds and regression detection in CI · Dependency and licence scanning, SBOM generation · Static application security testing at the commit stage · Dynamic scanning with OWASP ZAP against a deployed build · Accessibility testing and its automatable fraction · Triaging security findings without blocking all delivery
The maintenance discipline that decides whether a suite is believed in its third year. Diagnosing non-determinism by cause, quarantine policy with real expiry, test impact analysis so only affected tests run, and the measurements that make suite health a visible, owned property.
Topics: Sources of flakiness: timing, ordering, concurrency, shared state, environment · Detecting flakiness through reruns and historical trend analysis · Quarantine policy, ownership and repair deadlines · Test selection and impact analysis from change sets · Suite runtime budgets and keeping them flat as coverage grows · Coverage metrics: what they show and what they do not · Escaped-defect analysis to find the missing test layer
The assurance activities that only work with real traffic. Synthetic monitoring of critical journeys, canary analysis as an automated test of a release, feature-flagged exposure and experimentation, and chaos experiments that verify a system fails the way its designers believe it does.
Topics: Why some risks are only observable in production · Synthetic monitoring of critical user journeys · Canary analysis as an automated production test · Feature flags for controlled exposure and experimentation · A/B testing and validating behaviour, not just correctness · Chaos experiments: hypothesis, blast radius and abort criteria · Observability as a testing instrument · Feeding production findings back into earlier stages
We rebuild the agenda around the tools you actually run.
Request a custom agendaTake a real service, enumerate its failure risks, assign each to the earliest pipeline stage that can address it credibly, and produce a runtime budget per stage.
Replace a set of brittle cross-service end-to-end tests with consumer-driven contract tests, prove the coverage is equivalent, and measure the runtime and stability difference.
Convert a suite that depends on a shared dataset into one that seeds, isolates and resets its own data, then run two suites concurrently without interference.
Add a load test with a real traffic profile and a security scanning stage to a pipeline, baseline both, and tune thresholds until they catch a real regression without false alarms.
Run a suite repeatedly to expose non-deterministic tests, diagnose each cause, apply quarantine policy, then use test impact analysis to cut wall-clock time substantially.
Deliver a complete testing strategy for one real service — layers, stages, data, environments, non-functional checks, suite health measures and production synthetics — with before-and-after numbers.
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.
Terms. Course material remains licensed to the attendee. Read the terms.
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Certificate of completion
Great learning experience from a very knowledgeable instructor with well-prepared course notes. The lab exercises on AWS instance work well to learn the hands-on side of the course.
Rajesh is a very good trainer I have experienced in DevSecOps training. The number of contents in different topics he has posted on the DevOpsSchool public website are amazing and user friendly for beginners and experienced professionals.
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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