Advanced Platform Engineering for Microservices and Kubernetes at Scale
Introduction and Strategic Relevance in Asia
Across Asia, organizations in financial services, e commerce, logistics, telecommunications, manufacturing, and public sector are rapidly modernizing their technology stacks.
Traditional monolithic systems are being replaced by microservices architectures that can respond faster to market changes, regulatory requirements, and customer expectations.
At the same time, Kubernetes has become the de facto standard for container orchestration, enabling consistent deployment and operations across on premises data centers, regional hosting providers, and global clouds.
This shift is particularly pronounced in Asian markets where customer volumes are high, digital adoption is fast, and competition is intense.
Platform engineering capabilities are now a critical differentiator.
Enterprises that can design and operate robust internal platforms for microservices and Kubernetes gain the ability to:
- Launch new digital products in weeks instead of months.
- Scale seamlessly during seasonal peaks such as Singles Day, Ramadan, Golden Week, and year end sales.
- Meet strict regulatory and data residency requirements across multiple Asian jurisdictions.
- Reduce outages that impact brand reputation and revenue.
Many organizations in Asia have already containerized applications but struggle with fragmented clusters, inconsistent environments, and manual processes.
Advanced platform engineering addresses these gaps by providing a standardized, secure, and automated foundation on top of Kubernetes, aligned with business outcomes.
This intensive professional training is designed to help technical leaders, architects, and senior engineers build and operate such platforms in real world Asian enterprise contexts.
The Business Case and ROI for HR and People Managers
Investing in advanced platform engineering skills is not just a technology decision. It is a strategic business investment with measurable returns.
Key Business Outcomes
-
Faster time to market.
Standardized deployment pipelines and self service platforms reduce lead time for changes, enabling more frequent and safer releases. -
Improved reliability.
Properly engineered Kubernetes platforms with observability, resilience patterns, and capacity planning reduce unplanned downtime. -
Cost optimization.
Right sizing clusters, autoscaling, and efficient resource management can significantly lower infrastructure spend, especially in multi region Asian deployments. -
Talent retention.
Providing engineers with structured career development in modern platform engineering helps retain high demand cloud native talent.
Why HR and L&D Should Sponsor
- Aligns technology capability with digital transformation and innovation roadmaps that many Asian boards now demand.
- Reduces dependence on expensive external consultants by building internal platform expertise.
- Creates cross functional collaboration between development, operations, security, and compliance teams.
- Provides a structured, outcome focused learning pathway that can be tied to performance and promotion frameworks.
The program is designed so that participants return with actionable reference architectures, platform design patterns, and implementation roadmaps.
These can be immediately applied to existing microservices initiatives and Kubernetes environments, creating visible value within weeks.
Course Objectives
By the end of this training, participants will be able to:
- Design platform architectures that support large scale microservices deployments on Kubernetes across multiple environments.
- Define and implement platform engineering principles, roles, and operating models within their organization.
- Build secure, standardized, and reusable infrastructure templates for Kubernetes clusters and supporting services.
- Establish robust CI and CD pipelines for microservices, integrating testing, security checks, and policy enforcement.
- Apply service mesh, API gateway, and traffic management patterns to handle complex inter service communication.
- Implement observability stacks for metrics, logging, and tracing to support reliability and performance objectives.
- Introduce self service capabilities for development teams while maintaining governance and compliance.
- Plan and execute migration paths from monolithic or legacy systems to microservices running on Kubernetes.
- Optimize cluster capacity, autoscaling, and cost, especially relevant for high volume Asian markets.
- Define a pragmatic roadmap for platform maturity tailored to the organization’s size, sector, and regulatory context.
Detailed Syllabus
Module 1. Platform Engineering Foundations in the Cloud Native Era
- From DevOps to platform engineering. Why internal platforms are becoming essential.
- Core concepts. Product thinking, platform as a product, and treating developers as customers.
- Typical platform components on Kubernetes. Clusters, registries, pipelines, observability, and self service portals.
- Reference models and maturity stages for Asian enterprises of different sizes.
- Aligning platform goals with business and regulatory drivers in Asia Pacific.
Module 2. Advanced Microservices Architecture for Scalable Platforms
- Revisiting microservices principles with a platform engineering lens.
- Domain driven design patterns that support platform standardization.
- Service boundaries, data ownership, and communication patterns in high throughput environments.
- Resilience patterns. Circuit breakers, bulkheads, retries, timeouts, and back pressure.
- Patterns for multi tenant and multi region architectures common in Asian markets.
Module 3. Kubernetes Architecture and Cluster Design at Scale
- Cluster topology choices. Single cluster, multi cluster, and hybrid cluster designs.
- Designing for high availability across zones and regions.
- Networking fundamentals. CNI, ingress controllers, and service discovery.
- Security basics. Namespaces, RBAC, network policies, and secrets management.
- Standardizing cluster provisioning through infrastructure as code.
Module 4. Infrastructure as Code and Environment Standardization
- Choosing tooling for infrastructure as code such as Terraform or equivalent.
- Designing reusable modules for clusters, networking, storage, and security policies.
- Environment promotion strategies across development, testing, staging, and production.
- Managing configuration and secrets across multiple Asian regions and environments.
- Governance and change management for infrastructure code repositories.
Module 5. CI and CD Pipelines for Microservices on Kubernetes
- Pipeline design principles for large microservices portfolios.
- Container build best practices, image scanning, and provenance.
- Deployment strategies. Rolling updates, blue green, canary, and progressive delivery.
- Integrating automated testing, security checks, and policy as code into pipelines.
- GitOps concepts and implementation patterns for Kubernetes platforms.
Module 6. Service Mesh, API Gateways, and Traffic Management
- When and why to adopt a service mesh in an enterprise context.
- Core capabilities. mTLS, traffic shifting, retries, circuit breaking, and observability.
- API gateway patterns for internal and external consumers.
- Managing versioning, backward compatibility, and deprecation at scale.
- Designing secure ingress and egress for regulated industries such as banking and healthcare.
Module 7. Observability, Reliability, and SRE Practices on Kubernetes
- Building a complete observability stack. Metrics, logs, and distributed tracing.
- Defining SLIs, SLOs, and error budgets for microservices platforms.
- Incident management workflows and on call practices.
- Capacity planning, autoscaling, and performance tuning for Asian traffic patterns.
- Using chaos and game days to validate platform resilience.
Module 8. Security, Compliance, and Governance for Enterprise Platforms
- Security by design principles for Kubernetes and microservices.
- Image security, runtime protection, and vulnerability management.
- Policy as code. Admission controllers and guardrails for safe self service.
- Compliance considerations for Asian data protection and financial regulations.
- Auditability, logging, and reporting for internal and external stakeholders.
Module 9. Self Service Developer Experience and Platform Product Management
- Designing golden paths and templates for teams adopting the platform.
- Developer portals, catalogs, and documentation practices.
- Defining platform SLAs, roadmaps, and feedback loops with user teams.
- Balancing flexibility with standardization across diverse business units.
- Measuring platform success with quantitative and qualitative metrics.
Module 10. Migration Strategies and Platform Roadmapping
- Assessing current state architectures and identifying migration candidates.
- Strangler patterns and incremental modernization of legacy systems.
- Managing data migration, integration, and coexistence with legacy platforms.
- Phased rollout strategies across multiple business units and regions.
- Creating a 12 to 24 month platform engineering roadmap tailored to the organization.
Training Methodology
The program is delivered using a highly interactive and practical approach that reflects real world platform engineering work:
- Scenario based learning. Participants work through realistic scenarios drawn from Asian industries such as banking, retail, logistics, and telecommunications.
- Architecture workshops. Small group sessions where teams design platform blueprints and receive structured feedback.
- Hands on labs. Guided exercises on microservices deployment, Kubernetes configuration, observability, and pipeline automation using sample applications.
- Design reviews. Participants bring their own challenges and environments for facilitated discussion and peer learning.
- Action planning. Each participant creates a practical post training action plan linked to their organization’s roadmap.
The course can be delivered as an intensive multi day workshop or as a modular program spread over several weeks, depending on organizational needs and schedules across different Asian locations.
Who Should Attend
This advanced program is designed for technical professionals and leaders who are already familiar with basic container and Kubernetes concepts and who now need to scale and industrialize their approach:
- Platform engineers and members of internal platform or enablement teams.
- DevOps engineers and site reliability engineers responsible for production environments.
- Solution and enterprise architects designing microservices based systems.
- Senior backend and full stack engineers moving into platform focused roles.
- Technical leads and engineering managers overseeing multiple product teams.
- Cloud infrastructure and operations teams that manage Kubernetes clusters.
- Security engineers who need to integrate controls into the platform itself.
HR, L&D, and technology leaders can nominate cross functional cohorts to accelerate alignment between development, operations, and security teams, especially across distributed teams in different Asian cities or countries.
Frequently Asked Questions
What are the recommended prerequisites for participants?
Participants should be comfortable with basic Linux commands, container concepts, and fundamental Kubernetes objects such as pods, deployments, and services.
Experience with at least one programming language and prior exposure to CI and CD practices will help participants gain maximum value.
Experience with at least one programming language and prior exposure to CI and CD practices will help participants gain maximum value.
Is this course suitable for organizations that are still running mostly monolithic applications?
Yes. Many Asian enterprises are in transition.
The course covers migration patterns and how to design a platform that can support both modern microservices and adapted legacy workloads during the transition period.
The course covers migration patterns and how to design a platform that can support both modern microservices and adapted legacy workloads during the transition period.
Can the content be tailored to our industry or regulatory environment?
The program can be customized with sector specific case studies and regulatory considerations, for example for financial services, healthcare, or public sector in particular Asian countries.
Pre course discovery sessions can be arranged to align examples and exercises with your context.
Pre course discovery sessions can be arranged to align examples and exercises with your context.
What is the typical duration of the program?
A common format is three to four intensive days for core content, plus optional follow up clinics.
However, modular delivery over several weeks with shorter virtual sessions is also available for teams distributed across multiple locations.
However, modular delivery over several weeks with shorter virtual sessions is also available for teams distributed across multiple locations.
Does the course include hands on labs, or is it purely theoretical?
The course includes significant hands on components.
Participants will work through labs related to microservices deployment, Kubernetes configuration, observability, and pipeline automation using guided exercises and reference implementations.
Participants will work through labs related to microservices deployment, Kubernetes configuration, observability, and pipeline automation using guided exercises and reference implementations.
What outcomes can managers expect to see after the training?
Managers can expect clearer platform architectures, improved alignment between teams, and practical implementation plans.
Over time, this typically results in more predictable releases, better system reliability, and more efficient use of infrastructure resources.
Over time, this typically results in more predictable releases, better system reliability, and more efficient use of infrastructure resources.
Can mixed skill level teams attend together?
Mixed skill groups are welcome, provided participants meet the basic prerequisites.
Exercises are structured so that more experienced engineers can tackle advanced tasks while others focus on core patterns and concepts, encouraging peer learning and knowledge sharing.
Exercises are structured so that more experienced engineers can tackle advanced tasks while others focus on core patterns and concepts, encouraging peer learning and knowledge sharing.