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Platform Engineering โ€ข 2026 Specialization

Cloud, DevOps & MLOps Engineering

Architect Multi-Region Cloud Platforms, Kubernetes GitOps & Continuous ML Retraining Pipelines

Master the platform engineering discipline powering modern software and AI. Build automated cloud infrastructure with Terraform IaC, orchestrate production Kubernetes clusters on AWS EKS, implement GitOps deployments with ArgoCD, and deploy autonomous MLOps pipelines with MLflow.

7 Months Duration
320+ Hrs Live Mentor Labs
1-on-1 Mentor Guidance
AWS Multi-AZ Dedicated EKS Labs
Cloud DevOps and MLOps 3D Multi-Cloud Visual
Upcoming 2026 Batch Admissions
Limited 25 Seats โ€ข Weekend & Evening Batches

Get Expert Course Guidance

Speak with a Principal Architect & get a customized roadmap, syllabus, and 0% EMI plans.

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100% Confidential 15-Min Fast Callback 100% Placement Assistance
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Dual Certification Industry Recognized Certification
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Multi-AZ AWS Cluster AWS EKS & Terraform Cloud Lab
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1:1 Technical Mentorship Architecture Reviews by Lead Engineers
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100% Placement Assistance Dedicated Interview Prep & Hiring Partner Drives
PLATFORM ENGINEERING REALITY

Beyond Basic Bash Scripts & Click-Ops

Manual console clicking is a liability. Modern engineering organizations demand immutable infrastructure as code, declarative GitOps deployments, autonomous Kubernetes scaling, and automated MLOps pipelines.

01

Infrastructure as Code & AWS Cloud

Define production networks, compute clusters, and databases declaratively with Terraform. Implement state locking, reusable modules, and automated drift detection across multi-AZ environments.

02

Production Kubernetes & GitOps with ArgoCD

Deploy hardened Kubernetes clusters on AWS EKS with Cilium eBPF networking and Karpenter autoscaling. Synchronize production workloads automatically from Git repositories using ArgoCD.

03

Autonomous MLOps & AI Pipeline Engineering

Go beyond conventional web applications. Build automated machine learning pipelines with Kubeflow, manage model registries with MLflow, and trigger automatic retraining upon statistical data drift.

COMPREHENSIVE SYLLABUS

Industry-Aligned Practical Modules

A comprehensive curriculum engineered by Principal SREs and Cloud Architects. Progress from Linux networking and Terraform IaC to Kubernetes cluster engineering, GitOps CI/CD, and enterprise MLOps.

MODULE 01 โ€ข 7 WEEKS

Linux Internals, Cloud Networking & Terraform IaC

Master core systems administration, cloud networking topologies, and immutable infrastructure provisioning with Terraform.

MODULE 1.1
Linux Kernel, Systemd & Networking Diagnostics
Process namespaces, cgroups, systemd services, TCP/IP stack tuning, iptables, and network troubleshooting with netstat/tcpdump.
MODULE 1.2
AWS Multi-AZ Cloud Architecture & Security
VPC peering, private subnets, NAT gateways, Route 53 DNS routing, IAM roles with least-privilege policies, and CloudWatch.
MODULE 1.3
Terraform HCL, Modularization & State Locking
Reusable modules, input variables, remote state in S3 with DynamoDB locking, terraform plan validation, and workspace isolation.
MODULE 1.4
Infrastructure Testing & Automated Drift Detection
Terratest in Go, tflint static analysis, Trivy IaC security scans, and scheduled drift reconciliation workflows.
HANDS-ON LIVE PROJECT: Multi-Tier High-Availability AWS Cloud Infrastructure Provisioned Entirely via Modular Terraform with Remote State Locking.
MODULE 02 โ€ข 7 WEEKS

Production Kubernetes (EKS), eBPF & GitOps with ArgoCD

Architect highly scalable Kubernetes clusters, optimize pod scheduling, and automate continuous application delivery with GitOps.

MODULE 2.1
Kubernetes Internals & EKS Cluster Engineering
API server, etcd consensus, controller managers, kube-proxy, worker node pools, and AWS EKS managed control planes.
MODULE 2.2
Next-Gen CNI Networking with Cilium eBPF
eBPF kernel-level packet filtering, Cilium network policies, service mesh capabilities, and transparent encryption.
MODULE 2.3
Dynamic Node Autoscaling with Karpenter
Replacing legacy cluster autoscalers with Karpenter, rapid EC2 spot instance provisioning, and bin-packing optimization.
MODULE 2.4
Declarative GitOps Delivery with ArgoCD
Application CRDs, Helm chart management, automated sync loops, health checks, and multi-cluster GitOps orchestration.
HANDS-ON LIVE PROJECT: Self-Healing Production Kubernetes Cluster on AWS EKS with Cilium eBPF, Karpenter Autoscaling, and ArgoCD GitOps Sync.
MODULE 03 โ€ข 7 WEEKS

DevSecOps Pipelines, Canary Deployments & SRE Observability

Build automated security gates into CI/CD pipelines, execute zero-downtime canary rollouts, and implement enterprise observability.

MODULE 3.1
Enterprise CI/CD with GitHub Actions
Self-hosted runners, OIDC authentication with AWS, caching strategies, and automated artifact publishing to Amazon ECR.
MODULE 3.2
Container Security, Trivy & Runtime Protection
Vulnerability scanning with Trivy, admission controllers with OPA Gatekeeper, and runtime threat detection with Falco.
MODULE 3.3
Zero-Downtime Progressive Delivery with Argo Rollouts
Blue/green switching, canary traffic splitting with AWS ALB, automated rollback triggers based on error budgets.
MODULE 3.4
Prometheus, Grafana & OpenTelemetry SRE Dashboards
PromQL queries, Grafana dashboards, Golden Signals of SRE, OpenTelemetry distributed traces, and Alertmanager routing.
HANDS-ON LIVE PROJECT: Production DevSecOps Deployment Pipeline with Automated Vulnerability Scans, Canary Rollouts, and SRE Alerting.
MODULE 04 โ€ข 7 WEEKS

Autonomous MLOps, Model Registries & Kubeflow Serving

Productionize machine learning architectures, track experiments, orchestrate automated retraining pipelines, and monitor model drift.

MODULE 4.1
MLflow Tracking, Artifacts & Model Registry
Experiment metadata tracking, model versioning, transition stages (Staging, Production), and artifact storage in S3.
MODULE 4.2
Feature Store Architecture with Feast
Offline stores (Snowflake/Parquet), low-latency online stores (Redis), point-in-time joins, and feature serving.
MODULE 4.3
Kubeflow Pipelines on Kubernetes Clusters
Containerized pipeline components, DAG execution on EKS, volume mounting, GPU resource slicing, and artifact passing.
MODULE 4.4
Model Drift Detection & Continuous Retraining Loops
Data drift and concept drift monitoring with Evidently AI, automated pipeline triggers on drift detection, and shadow deployment.
HANDS-ON LIVE PROJECT: End-to-End Autonomous MLOps Platform with Kubeflow, MLflow Registry, and Automated Retraining Loops Triggered by Data Drift.
PLATFORM PORTFOLIO

Industry Platform Engineering Live Projects

Build enterprise-grade cloud platforms and MLOps architectures that demonstrate deep platform leadership to senior engineering hiring managers.

LIVE PROJECT 01 โ€ข KUBERNETES GITOPS

Multi-Region Kubernetes GitOps Platform on AWS

A production multi-AZ AWS EKS cluster provisioned via Terraform, automated by ArgoCD, and equipped with Cilium eBPF networking and Karpenter autoscaling.

INFRASTRUCTURE: Terraform Modular Architecture + Remote S3 State
ORCHESTRATION: AWS EKS v1.31 + Karpenter Just-In-Time Node Scaling
NETWORKING: Cilium eBPF CNI with strict L3/L4/L7 network policies
DEPLOYMENT LOOP: ArgoCD multi-environment repository synchronization
LIVE PROJECT 02 โ€ข DEVSECOPS & SRE

Enterprise DevSecOps Pipeline with Canary Rollouts

An end-to-end automated deployment pipeline with integrated Trivy vulnerability scanning, OPA Gatekeeper policies, and Argo Rollouts canary deployments.

CI RUNNER: GitHub Actions with AWS OIDC authentication
SECURITY GATES: Trivy vulnerability gates + OPA admission control
CANARY STRATEGY: Argo Rollouts automated traffic shift (10% โ†’ 50% โ†’ 100%)
OBSERVABILITY: Prometheus metric evaluation with auto-rollback triggers
LIVE PROJECT 03 โ€ข AUTONOMOUS MLOPS

Autonomous MLOps Continuous Retraining Pipeline

A production model delivery platform that tracks experiments with MLflow, runs distributed training on Kubeflow, and automatically retrains on data drift.

PIPELINE RUNNER: Kubeflow Pipelines on EKS with GPU allocation
MODEL REGISTRY: MLflow Model Registry with versioning stages
FEATURE STORE: Feast Feature Store with Redis online cache
DRIFT DETECTION: Evidently AI monitoring triggering automated retraining
ENTERPRISE TOOLCHAIN

Master the Modern Cloud & MLOps Stack

Learn the exact cloud infrastructure, container orchestration, and MLOps tools expected by high-scale tech organizations.

IAC
Terraform
Declarative infrastructure as code, modular architecture, and state locks
CONTAINER ORCH
Kubernetes (EKS)
Production cluster design, CKA exam alignment, and pod scheduling
CLOUD PLATFORM
AWS Cloud
VPCs, IAM roles, EKS, ECR, S3, RDS, CloudWatch, and Route 53
GITOPS
ArgoCD
Declarative continuous delivery and GitOps reconciliation loops
CI/CD
GitHub Actions
Automated build, test, and container release pipelines with OIDC
PACKAGING
Helm 3
Kubernetes application packaging, templating, and versioning
OBSERVABILITY
Prometheus & Grafana
Metrics collection, PromQL alerts, and SRE telemetry dashboards
NETWORKING
Cilium eBPF
High-performance kernel networking, security policies, and mesh
MLOPS CORE
MLflow
Model registry, experiment tracking, and production stage lifecycle
ML PIPELINES
Kubeflow
Cloud-native machine learning workflows and GPU orchestration
SECURITY
Trivy & Falco
Container vulnerability scanning and runtime threat detection
AUTOSCALING
Karpenter
High-speed node autoscaling and cost optimization on AWS
ADMISSIONS โ€ข 2026 SPECIALIZATION

Apply for Cloud, DevOps & MLOps Engineering

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COMMON QUESTIONS

Frequently Asked Questions

Direct answers regarding certifications, cloud sandbox credits, MLOps vs DevOps, and career assistance.

Does this course prepare me for CKA (Kubernetes) and AWS certifications?+
Yes. The syllabus aligns comprehensively with the domains of the Certified Kubernetes Administrator (CKA), Terraform Associate, and AWS Certified Solutions Architect - Associate exams.
What are the batch options and locations?+
Learners can attend classroom training in our Noida technical campus or join live interactive online sessions from anywhere in India. Weekend batches (Saturday-Sunday) are specially structured for working system admins, developers, and engineers.
What cloud lab and practical environments are provided?+
Every learner receives dedicated cloud lab environments on AWS to provision multi-AZ VPC networks, manage elastic Kubernetes (EKS) clusters, deploy ArgoCD GitOps pipelines, and build automated CI/CD workflows.
Why is MLOps included alongside DevOps?+
In 2026, leading enterprises are deploying AI and machine learning models alongside traditional software. MLOps (model registry, feature stores, drift monitoring, MLflow) represents the fastest-growing salary segment in the cloud industry.
How does 4Achievers support my job placement?+
We provide comprehensive career support: resume restructuring for Cloud/DevOps roles, live scenario-based mock interviews with senior DevOps leads, infrastructure portfolio building on GitHub, and direct interview scheduling with top Indian tech employers.
Can freshers or developers without cloud experience join?+
Yes. We start Module 01 with Linux administration, shell scripting, Git, and networking fundamentals before advancing to Docker, Terraform, Kubernetes, and automated release engineering.