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43 courses
Learn containers from fundamentals to debugging and production practices.
Master Linux for DevOps, SRE, and cloud engineering: OS architecture, filesystem, commands, text processing, users, permissions, services, storage, networking, SSH, security, cheatsheet, and production troubleshooting.
Master production-ready Bash automation for DevOps and SRE: safe scripting, variables, arguments, logic, loops, functions, pipelines, traps, validation, testing, CI/CD, security, cheatsheet, and troubleshooting.
Master practical networking for DevOps, SRE and cloud engineering: packet flow, OSI/TCP-IP, Ethernet, IPv4/IPv6, CIDR, routing, NAT, TCP/UDP, DNS, HTTP/TLS, load balancing, Linux networking, cloud/container/Kubernetes networking, observability, production debugging, command playbooks and a final cheatsheet.
Master YAML syntax, production-safe configuration, validation, debugging, security, and DevOps tool patterns.
Master Jenkins Pipeline-as-Code from Groovy/DSL foundations to production CI/CD, agents, credentials, Docker, Shared Libraries, troubleshooting, security, and delivery patterns.
Master Kubernetes architecture and production operations: control plane, nodes, workloads, networking, storage, scheduling, security, kubectl, observability, debugging, operations and a complete cheatsheet.
Master Terraform from IaC fundamentals and HCL through providers, state, modules, backends, testing, security, CI/CD, imports, refactoring, debugging and a complete cheatsheet.
Production configuration management and automation with inventory, playbooks, modules, variables, Jinja2, roles, collections, Vault, dynamic inventory, rolling updates, execution environments, CI/CD, security, troubleshooting and a final cheatsheet.
Master AWS from cloud engineering fundamentals through IAM, networking, compute, storage, databases, containers, serverless, observability, security, cost, reliability, operations, debugging and a complete cheatsheet.
Production AWS infrastructure as software with CDK v2: constructs, stacks, synthesis, CloudFormation, bootstrapping, assets, IAM, networking, data, reusable platform constructs, testing, pipelines, security, multi-account delivery, migration, troubleshooting and a final cheatsheet.
Master Microsoft Azure from Entra identity and resource hierarchy through networking, compute, storage, databases, AKS, Container Apps, App Service, Functions, security, observability, IaC, reliability, operations, debugging and a complete cheatsheet.
Master Google Cloud from resource hierarchy and IAM through networking, compute, storage, databases, GKE, Cloud Run, security, observability, IaC, reliability, operations, debugging and a complete cheatsheet.
Turn technical skills into job-ready proof: resumes, portfolio, applications, networking, interviews, troubleshooting, SRE/cloud role prep, offers, and first-90-days success.
Master Git and GitHub for professional engineering: Git internals, staging and history, branches, merge/rebase, recovery, remotes, pull requests, reviews, rulesets, CODEOWNERS, releases, authentication, Git LFS, signed commits, debugging, team strategy and a final cheatsheet.
Production CI/CD with GitHub Actions: workflow architecture, triggers, runners, expressions, permissions, artifacts, cache, matrices, reusable workflows, OIDC, Docker, Kubernetes, Terraform, supply-chain security, debugging and a final cheatsheet.
Production metrics and observability for DevOps/SRE: Prometheus architecture, instrumentation, exporters, PromQL, histograms, recording and alerting rules, Alertmanager, TSDB, remote write, Kubernetes monitoring, Grafana dashboards/alerting/provisioning, SLOs, cardinality, security, debugging, command playbook and cheatsheet.
Production reliability engineering for DevOps/SRE: SLIs/SLOs/error budgets, reliability math, alerting, toil, on-call, incidents, troubleshooting, postmortems, capacity, overload, resilience patterns, HA/DR, safe releases, reliability testing, production readiness, debugging and cheatsheet.
Production logging, search and observability with Elasticsearch, Kibana, Logstash, Elastic Agent/Fleet, ECS, data streams, lifecycle, OpenTelemetry, Kubernetes, security, performance, debugging and cheatsheet.
Production GitOps and Argo CD: reconciliation, Applications, ApplicationSets, Helm/Kustomize, multi-cluster delivery, RBAC, projects, security, progressive delivery, observability, troubleshooting and cheatsheet.
Production engineering foundations for building and operating production-ready services: ownership, service lifecycle, architecture, PRRs, launch risk, reproducibility, safe change, observability, health, alerting, documentation, on-call, incidents, troubleshooting, automation, performance, capacity, resilience, dependencies, data, security, cost, testing, maturity, platform standards and operational evidence.
Production incident management and sustainable on-call engineering: declaration, severity, incident command, triage, mitigation, communication, escalation, handoffs, paging, alert quality, runbooks, major incidents, incident metrics, simulations and operational evidence.
Evidence-driven troubleshooting across applications, Linux, networking, DNS, TLS, containers, Kubernetes, cloud, databases, Redis, queues/Kafka, storage, CI/CD, GitOps, configuration, dependencies, performance, capacity, distributed systems and production-safe mitigation.
Production performance engineering across latency, throughput, concurrency, workload modeling, k6, JMeter, profiling, Linux, networking, Kubernetes, databases, caches, queues, autoscaling, CI performance gates, soak/spike/breakpoint testing and evidence-driven optimization.
Production capacity engineering across demand modeling, forecasting, headroom, CPU/memory/storage/network/database/queue capacity, Kubernetes and cloud autoscaling, failure reserve, quotas, cost-aware scaling, overload protection and capacity incident prevention.
Production high availability and disaster recovery across failure domains, redundancy, RTO/RPO, multi-zone and multi-region design, database and storage resilience, backup/PITR, failover/failback, cyber recovery, DR testing, capacity and evidence-driven recovery.
Production chaos engineering and resilience testing with steady-state hypotheses, controlled fault injection, blast-radius and stop controls, Kubernetes and cloud chaos platforms, game days, CI/CD resilience gates, failure-mode validation and evidence-driven recovery.
Safe production delivery with immutable artifacts, progressive rollout, compatibility, release gates, GitOps, rollback, observability, supply-chain evidence and release verification.
Production application resilience patterns: deadlines, timeouts, retries, backoff, jitter, idempotency, circuit breakers, bulkheads, rate limiting, load shedding, graceful degradation, backpressure, caching, consistency, and recovery.
End-to-end production engineering capstone integrating architecture, SLOs, IaC, Kubernetes, delivery, observability, performance, capacity, reliability patterns, chaos, incidents, HA/DR, recovery and operational evidence.
Production-grade OpenTelemetry instrumentation and telemetry engineering across traces, metrics, logs, context propagation, semantic conventions, SDKs, OTLP, Collector pipelines, Kubernetes, sampling, security, scaling and backend integration.
Production database reliability across transactions, isolation, locking, connection pools, indexing, query plans, replication, backups, PITR, failover, schema changes, PostgreSQL, MySQL, managed cloud databases, capacity, security and incident troubleshooting.
Production Redis reliability across memory, eviction, TTLs, key design, persistence, replication, Sentinel, Cluster, caching patterns, Streams, security, capacity, observability, failover, recovery and incident troubleshooting.
Production reliability engineering for event-driven and messaging systems: delivery semantics, ordering, idempotency, outbox/inbox, retries, DLQs, backpressure, queues, streams, consumer lag, observability, HA/DR, platform semantics, incident response and safe replay.
Production Apache Kafka event-streaming engineering: KRaft, topics and partitions, producers, consumers, share groups, durability, transactions, schemas, Connect, Streams, CDC, multi-cluster replication, tiered storage, security, performance, monitoring, recovery and troubleshooting.
Engineering-focused prompt design for turning goals, context and constraints into verifiable code, architecture, DevOps, cloud, troubleshooting, security, performance and production outcomes with safe AI-assisted workflows.
A self-guided 30-day hands-on Python path from fundamentals to intermediate engineering: syntax, data structures, functions, files, debugging, virtual environments, pip, PEP style, testing, OOP, APIs, concurrency, robust automation and progressive real-world projects.
A self-guided 30-day hands-on JavaScript path from fundamentals to intermediate engineering: modern ECMAScript ES2015+, browser and Node.js runtimes, modules, npm, asynchronous programming, debugging, testing, secure and robust coding, and progressive real-world projects.
Case-study-driven DevOps and CI/CD learning: six research-backed real-company transformations and four realistic scenarios, with before/after architecture, delivery metrics, trade-offs, decision points, modern reference architectures, and a capstone transformation plan.
Exam-focused SAA-C03 preparation with official-blueprint lessons, architecture decisions, hands-on reinforcement, 320 original scenario-question variations built from a curated decision bank, a free diagnostic, four learn-mode domain banks, and three timed 65-question mock exams.
Hands-on Certified Kubernetes Administrator preparation aligned to the current Kubernetes v1.35 blueprint: 60 lessons, a free practical diagnostic, five domain task banks, 134 original practical tasks, and two 120-minute performance mocks.
Advanced AWS DevOps certification preparation aligned to DOP-C02: 80 blueprint-focused lessons, 401 original scenario-question variations, a free diagnostic, six domain Learn Mode banks, and three 75-question / 180-minute timed mocks.
Performance-based Certified Kubernetes Security Specialist preparation aligned to the current Linux Foundation CKS blueprint: 73 detailed lessons, 165 original hands-on security tasks, six domain labs, a free diagnostic, and two timed mocks.