DevOps Foundation : Fundamentals to start a DevOps career

Explore the real purpose of DevOps by examining time to market and cost of delay, tracing its roots from waterfall, agile, and lean to culture and myths surrounding DevOps.

DevOps redefines market competition by uniting business and IT to deliver quality software faster. Embrace agile, lean, and IT service management practices to reduce silos and cut time to market.

DevOps reduces latency by automating bottlenecks, shortening time to market. Time to market is the duration from feature request receipt to user receiving value, crucial for business.

Define the cost of delay and analyze its impact on time to market, missed market windows, penalties, and the monetary value of postponed benefits in DevOps projects.

Trace the origins of DevOps and how IT management shifted from focusing on IT systems to delivering IT services through evolving best practices and standards.

Agile emphasizes closer customer and developer interaction, small batch sizes, and 2 to 4 week sprints delivering viable products with fast feedback, but IT operations can limit rapid deployment benefits.

Learn lean software development and its Toyota-inspired origins in lean manufacturing. Apply Kaizen, PDCA, and Just-in-time concepts to eliminate seven types of waste and improve value in DevOps.

Learn infrastructure as code to manage networks, virtual machines, storage, and load balancers using descriptive models like JSON templates, enabling repeatable environments across test, dev, and production.

Explore how virtualization converts hardware into software, reducing costs, accelerating deployments, and enabling infrastructure as code for DevOps and cloud readiness.

Cloud computing shifts from capex to opex, enabling rapid elasticity and pay-as-you-go scaling that makes global infrastructure available on demand, while DevOps and infrastructure as code power efficient deployment.

Explore containerization as a lightweight, portable alternative to virtual machines, enabling fast starts, small images, and seamless deployments with Azure container apps, plus the shared responsibility model.

Trace DevOps origins from agile and lean development to infrastructure as code, virtualization, and cloud computing. Observe how these roots form a culture and agile process across the value chain.

Discover the benefits of DevOps, including faster time to market, reduced technical debt, and eliminated fragility for robust, low-bug software with high availability.

DevOps accelerates time to market by reducing batch size, automating deployments, enabling autonomous teams, and continuous improvement, delivering measurable gains like 20% faster time to market and 22% quality improvements.

Learn how DevOps promotes reducing technical debt by resolving bugs quickly, avoiding non-optimal fixes, and conducting root-cause analysis and tests to prevent accumulating future costs.

DevOps aims to eliminate fragility in legacy and highly integrated systems while antifragility strengthens systems under stress, as described by Netflix's Kaomoke.

Debunk DevOps myths and explain how DevOps extends Agile to the entire organization, emphasizing culture, mindset, and goals to reduce technical debt and fragility beyond tooling.

Explore the origins, benefits, and cultural impact of devops while learning its five principles—value stream mapping, deployment pipeline, version control, configuration management, and the definition of DUN.

Explore value stream mapping to map the as-is and to-be value streams, optimize end-to-end software delivery, and reduce waste by tracking lead time, process time, and value-adding activities.

Drive the value stream from version control to users by automating the deployment pipeline, which builds, tests, and deploys changes through automated environments with feedback and audit logs.

Learn how continuous delivery uses continuous integration to build a binary and run code and runtime checks, then progress through integration, system tests to enable production deployment via a pipeline.

Master version control as a core DevOps principle, storing all IT assets in one system. Track what changed, who changed it, and restore the system while safely removing unnecessary files.

Automate configuration management by using scripts in version control, enable automatic environment creation during deployment pipelines, and restrict production changes to scripted processes.

Define done in DevOps as delivering value to the customer when the value stream reaches production, not when individuals finish their tasks; emphasize end-to-end value, collective responsibility, and automated deployment.

Clarify the definition of done in DevOps, focusing on delivering user value, secure, encrypted data, and complete acceptance criteria before production release, demonstrated with a fund transfer feature.

Emphasizes five DevOps principles—Value Stream, Deployment Pipeline, Full-Version Control System, Automated Configuration Management, and the definition of DUN—and shows that adoption is essential for meaningful DevOps outcomes.

Explore the strategic value of a diverse, cross-functional DevOps team with clear roles, full-time collaboration, shared tools, and a unified design, build, test, release pipeline.

Limit work in progress to improve DevOps workflow and team productivity. Set WIP limits on each Kanban stage to reduce backlog, improve flow, quality, and agility.

Explore how smaller batch size in DevOps accelerates delivery by breaking large tasks into smaller features, enabling CI/CD pipelines and Kanban tracking for faster, higher-quality releases.

Reduce rework, lower technical debt, and deliver faster by early defect detection through shift left, complemented by static code analysis, unit and integration tests, and CI/CD.

Adopt a startup mindset in DevOps to accelerate delivery through agility, rapid iteration, and customer value. Foster collaboration, empower teams, and embrace feedback-driven, incremental releases to continuously improve software.

Foster collaboration among development, operations, business, and customers to support innovation and continuous improvement in devops, encouraging ideas to automate tasks and speed the pipeline within allocated time and budget.

Explore how architecture thinking shapes DevOps decisions, with real stories on loose coupling, blue/green deployments, immutable infrastructure, and zero downtime deployments.

Compare the two architectures—tight coupling and loose coupling—from monoliths to microservices, showing how architecture choices power business velocity and DevOps.

Use the strangler fig pattern to replace a monolith incrementally with loosely coupled services, via a router or api gateway, delivering value from month one.

Master resiliency patterns—circuit breakers, bulkheads, and retries—to prevent cascading failures, isolate resources, and stabilize systems with exponential back-off and jitter during slow dependencies.

Explore blue/green, cannery, and rolling deployments to ship code with zero downtime, instant rollback, and controlled risk, while emphasizing backwards compatible database migrations and gradual change.

Apply the 12-factor app principles for cloud-native, scalable software: one code base, explicit dependencies, environment configuration, stateless processes, and repeatable build–release–run deployments.

Explore how DevOps differs from traditional software development and adopt the essential DevOps mindset as a cultural shift across the organization, embracing collaboration and a customer-centric approach.

Explore the five levels of modern DevOps and the career and salary roadmap from entry to staff architect, highlighting ci/cd, infrastructure as code, Kubernetes, platform engineering, and AI operations.

Identify your constraint skill that bottlenecks your career, then apply a four-step plan to close it: name, prove, bridge, and deadline, guided by an honest audit heat map.

Learn how to harmonize DevOps and ITSM by automating standard changes, governing major ones, and applying ETL 5's product thinking, AI integration, and governance for speed with structure.

Explore how DevOps philosophy drives fast, automated software delivery, and how Site Reliability Engineering maintains performance with telemetry and error budgets to protect the user experience.

Explore the ai infrastructure engineer, mastering compute clustering and orchestration, inference optimization, and retrieval augmented generation with vector databases to deploy scalable, reliable ai systems.

Identify when DevOps is feasible and valuable for business. Recognize readiness gaps, monolithic infrastructure and architecture limits, and embrace a flexible mindset to start small and build up.

Assess the applicability of DevOps, and identify when it fits or fails. Explore organizational culture, technology as a business enabler, rapid information technology change, and investment considerations.

Explore the limitations of DevOps, including dependence on internal development, staff versus contractors, culture change, and the shift from monolithic to microservices architectures.

Explore commercial off-the-shelf software (COTS), its quick installation and customization, and the implications for reliability, cost, delivery times, and managing COTS within a DevOps, mixed-technology landscape.

Understand cargo culting in DevOps, where teams imitate Kubernetes or the Spotify model without grasping underlying principles, and learn to apply tools only when they solve your problems.

Embrace culture change and Kaizen by merging dev and ops into small DevOps teams, automating environments, and open monitoring for shared responsibility across production.

Adopt DevSecOps to weave security into the delivery pipeline and shift-left testing from production today. Leverage security as code with SAST, SCA, and DAST in ci/cd pipelines for secure velocity.

Adopt AIOps to monitor systems with machine learning on metrics, logs, and traces, identify root causes, and enable self-healing infrastructure with continuous feedback at scale.

Discover DataOps and MLOps, data as code, and automated governance that power continuous training, bias checks, and secure, high-stakes AI in banking and aviation.