Private corporate batches, live online cohorts and 1-on-1 mentoring in configuration correctness — the data format that declares almost every modern system — 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 YAML as a correctness problem rather than a syntax lesson — the implicit typing rules behind the unquoted-boolean and leading-zero failures, block versus flow style, literal and folded scalars with their chomping indicators, and anchors, aliases and merge keys with the tooling divergence that makes them risky. Sessions cover the validation layer most teams are missing: JSON Schema over YAML, yamllint and kubeconform in CI, policy checks with conftest, and testing the rendered output of Helm and Jinja2 templates rather than the templates themselves.
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 YAML that means a trainer who has run it in production — configuration correctness — the data format that declares almost every modern system — 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 YAML batchYAML is a data serialization format, not a tool and not a language. It defines a small data model — scalars, sequences and mappings, arranged into documents and streams — and a human-oriented syntax for writing it down. Every JSON document is valid YAML 1.2, so YAML is best understood as JSON with indentation instead of braces, plus a set of conveniences: comments, multi-line strings, anchors for reuse, multiple documents in one file, and implicit typing that infers whether a bare token is a string, a number, a boolean, a timestamp or null.
That convenience is also the risk. Because YAML infers types, the unquoted value no becomes false, 08 is rejected as an invalid octal number under some parsers, 1.10 becomes the number 1.1, and a version string loses its trailing zero. Because it is indentation-sensitive, a single misplaced space silently changes a key's parent rather than raising an error. Because anchors and merge keys are resolved at parse time, a template that emits them can produce a document that is valid YAML and completely wrong. None of these produce syntax errors — they produce a well-formed document that means something other than what the author intended.
That matters because YAML is now the declarative surface of the entire infrastructure stack: Kubernetes manifests, CI pipeline definitions, configuration management playbooks, container compose files, OpenAPI specifications, cloud templates, policy bundles and application configuration. Treating it as a format worth understanding precisely — with schemas, linting and validation in CI — is the difference between catching a defect on a pull request and discovering it during a deployment.
Infrastructure moved into the repository, and the repository is full of YAML. A platform team of ten now maintains thousands of manifests, pipeline definitions, values files and policy documents, and every one of them is executable configuration with production consequences.
The tooling around that has not kept pace with the volume. Most organisations still merge YAML with no schema validation, no linting beyond whitespace, no test for the rendered output of a template, and no policy gate — so the first thing that checks the document is the cluster or the pipeline that applies it. The resulting incident class is recognisable everywhere: a boolean that was meant to be a string, a value indented under the wrong key so a limit was never applied, an anchor overridden in a way the author did not expect, a Helm template that emitted valid YAML with the wrong nesting, a secret committed in plaintext because YAML made it easy.
The skill in demand is not YAML syntax, which takes an afternoon. It is configuration correctness: knowing the failure modes, writing or adopting schemas, validating rendered output rather than templates, and putting the whole thing behind a CI gate so bad configuration cannot merge.
What YAML actually defines, separated from the tools that consume it. Nodes, scalars, sequences and mappings; documents, directives and streams; the relationship to JSON; block versus flow style; comments; and the version differences between YAML 1.1 and 1.2 that still cause real divergence between parsers.
Topics: Scalars, sequences and mappings — the whole data model · Documents, the --- separator, ... terminator and multi-document streams · YAML as a superset of JSON, and what that means in practice · Block style versus flow style, and when each is appropriate · Comments and why they do not survive most round-trips · YAML 1.1 versus 1.2 and parser divergence · Which parser your tool actually uses, and why it matters
The module that prevents the most incidents. How YAML decides whether a bare token is a string, integer, float, boolean, timestamp or null; the well-known failure cases; the differences between plain, single-quoted and double-quoted scalars; escape sequences; and explicit tags when inference must be overridden.
Topics: Implicit typing and the core schema resolution rules · Booleans: the unquoted no, y, on and off problem · Numbers: leading zeros, octal interpretation, underscores and version strings · Null, empty and the four ways to write nothing · Timestamps and dates inferred where a string was meant · Plain, single-quoted and double-quoted scalars and their escape rules · Explicit tags — !!str, !!int — and forcing a type · Unicode, special characters and reserved indicators
The layout rules and the block scalar forms that carry scripts, certificates and embedded configuration. Indentation as structure, nesting sequences inside mappings, the literal and folded styles, chomping and indentation indicators, and the whitespace mistakes that change meaning without producing an error.
Topics: Indentation as structure, and why tabs are forbidden · Nesting sequences within mappings and the optional-indent ambiguity · Complex keys and the ? indicator · Literal block scalars with | for scripts and keys · Folded block scalars with > and how line folding actually works · Chomping indicators: strip, clip and keep · Explicit indentation indicators for content starting with spaces · Long lines, wrapping and readability in review
YAML's built-in reuse mechanism, its practical uses in pipeline and compose files, and its limits. Anchors and aliases, the merge key and its override semantics, nesting and ordering rules, tool-specific extensions — and the reasons many teams deliberately forbid all of it.
Topics: Anchors and aliases: definition, reference and scope · The merge key and override precedence · Multiple merges and ordering · Reuse patterns in CI pipeline definitions · Where anchors break: cross-document, cross-file and templated output · Merge keys in YAML 1.2 and inconsistent parser support · Denial of service through alias expansion · Alternatives: templating, overlays and composition tools
Turning YAML from free text into something verifiable. JSON Schema applied to YAML documents, writing a schema for an internal configuration format, editor integration so mistakes surface while typing, and the ecosystem validators — yamllint, kubeconform, OpenAPI, CUE — with an honest comparison of what each catches.
Topics: Why syntax validity is not correctness · JSON Schema over YAML: types, required properties, enums and patterns · Writing a schema for an internal configuration format · Composition: allOf, oneOf, conditionals and defaults · Editor and language-server integration for live validation · yamllint for style and structural rules · Kubernetes manifest validation with kubeconform · OpenAPI as a schema-first YAML contract · CUE and typed configuration in outline · Choosing what to validate and what to leave open
The single largest source of YAML defects in modern estates: generating an indentation-sensitive format with a text templating engine that does not understand it. Go templates and Helm, Jinja2, the indentation and quoting failure modes, and the structural alternatives that avoid the problem entirely.
Topics: Text templating an indentation-sensitive format — the core problem · Go templates and Helm: values, defaults and conditionals · indent and nindent, and the errors they exist to solve · Quoting injected values: the toYaml and quote functions · Jinja2 templating in configuration management contexts · Whitespace control and the blank-line and trailing-space traps · Validating rendered output rather than the template · Structural alternatives: Kustomize overlays, jsonnet, CUE · Snapshot testing rendered manifests in CI
Operating a large YAML estate safely. Keeping secrets out of plaintext, the deserialization vulnerabilities that come from unsafe loading, the review and merge-conflict problems that come from thousands of near-identical files, and building the CI gate that ties every earlier module together.
Topics: Secrets in YAML: why base64 is not encryption · SOPS, sealed secrets and external secret stores · Secret scanning as a pre-commit and CI gate · Unsafe deserialization: arbitrary object construction and safe loading · Alias expansion limits and parser hardening · Merge conflicts, ordering and diff noise in large YAML files · Formatting, key ordering and canonicalisation for reviewable diffs · Policy as code with conftest and OPA · Assembling the full gate: lint, schema, policy, render, test · Documenting configuration so it survives its author
We rebuild the agenda around the tools you actually run.
Request a custom agendaPredict the parsed type of thirty ambiguous scalar values — booleans, leading zeros, version strings, timestamps, nulls — then verify each against two parsers and explain every disagreement.
Work through configuration files where indentation silently reparented a value, find each one without a syntax error to help you, and build a lint rule that catches the class.
Write a JSON Schema for a real internal configuration format, wire it into the editor and into CI, and prove that three previously-shipped defects now fail before merge.
Debug a Helm chart that renders syntactically valid YAML with incorrect nesting and unquoted injected values, then add rendered-output snapshot tests so it cannot regress.
Find plaintext credentials across a repository's YAML, migrate them to encrypted or externally referenced values, and add a scanner that blocks the next one.
Assemble lint, schema validation, manifest validation, policy checks and rendered-output tests into one CI gate, and demonstrate it blocking five distinct classes of bad configuration.
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
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.
Good discussion, helped us to understand different tools in SRE.
| 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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