Welcome to the portfolio of Deo Shankar, a seasoned Multi-Cloud Solutions Architect with over 13 years of experience in designing and implementing enterprise-scale cloud solutions.
Currently working as an Architect at Tiger Analytics, previously held positions at Oracle Corporation, Paytm, Xebia, Nagarro, Ericsson, and Wipro.
AWS Solutions Architect Professional, AWS Machine Learning Specialty, Google Cloud Professional Cloud Architect, Google Cloud Professional ML Engineer, Databricks Certified Data Engineer, Oracle Cloud Infrastructure Architect Professional
Email: jha.deo1771@gmail.com | Phone: +91-9891698505 | Location: Noida, India
LinkedIn: https://linkedin.com/in/deo-shankar | GitHub: https://github.com/deosha
Let's discuss how I can help with your cloud architecture needs
Or email directly: jha.deo1771@gmail.com
Phone: +91-9891698505
📧 jha.deo1771@gmail.com | 📱 +91-9891698505 | 🌐 deos.dev
Semantic Protocol Layer Translation for AI Agent Interoperability
Novel approach to bidirectional protocol translation enabling seamless communication between heterogeneous AI agents
secscan-cli
Multi-language security vulnerability scanner supporting Python, JavaScript, Java, and Go codebases
$3M lost in potential sales. Customer trust damaged. My architecture. My fault.
The Problem:
The 8-Week Rebuild:
Result: Scaled from 1M crash → 5M+ TPS success. No downtime since.
"Failure is the best teacher. This disaster made me the architect I am today."
You're not Google. You don't need K8s for 10 containers. ECS Fargate or even EC2 with systemd will save you hundreds of engineering hours. I've seen startups die while perfecting their K8s setup instead of shipping features.
N+1 query problems, complex caching, security nightmares (query depth attacks), and junior developers creating performance disasters. REST with proper versioning beats GraphQL 9 times out of 10. I've ripped out GraphQL from 3 projects and never looked back.
PostgreSQL > MongoDB for 95% of use cases. EC2 > Kubernetes for <100 containers. REST > GraphQL. Monolith-first > Microservices-first. I chose "boring" tech at Paytm and saved $2M/year while others debugged their "cutting-edge" distributed traces.
I write 70% less boilerplate now. GitHub Copilot, ChatGPT, and Claude are force multipliers. Developers fighting AI tools are like accountants refusing Excel in the 1980s. Adapt or become irrelevant.
Unless you're Netflix, pick one cloud and go deep. Multi-cloud means lowest common denominator features, 3x the complexity, and none of the expertise. I've seen more companies fail from multi-cloud complexity than from vendor lock-in.
"Strong opinions, loosely held. Prove me wrong and I'll buy you coffee ☕"
Director - Cloud Solution Engineering at Oracle
Managed Deo directly at Oracle
View on LinkedIn →"I have known Deo for last 2 years since the time he has joined Oracle. He has excellent technical knowledge around cloud solutions right from automation, IaaS, DevOps to Networking etc. He articulates his thoughts very well while delivering a session or having a deep technical conversation with customers or colleagues. His positive attitude and never say 'No' even in a toughest of situations is an asset for any organization. Above all a great human being."
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║ MULTI-CLOUD SOLUTIONS ARCHITECT v13.0.0 ║
║ [ AWS | GCP | OCI | AZURE | Databricks | Snowflake] ║
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Available Commands: ═══════════════════════════════════════════════════════════════════════════════ ## PROFILE whoami Display professional summary skills Show technical expertise experience List work experience projects View multi-cloud success stories certifications Display all certifications education Show educational background contact Get contact information stats Show performance metrics & GitHub activity ## TECHNICAL architecture View cloud architecture patterns blog Read technical publications ssh Connect to cloud environment docker Show running containers kubectl Display Kubernetes cluster status terraform Show infrastructure as code ## SYSTEM portfolio Return to portfolio view download-cv Download full CV clear Clear terminal help Show this help menu neofetch System information matrix Enter the Matrix exit Exit terminal mode Type a command to explore...
USER PROFILE LOADED ═══════════════════════════════════════════════════════════════════════════════ Name: Deo Shankar Role: Multi-Cloud Solutions Architect Focus: AI/ML & Generative AI Experience: 13+ Years Location: Noida, India ## SUMMARY Solutions Architect with 13+ years designing distributed systems at scale. Specialized in AWS cloud architecture, ML infrastructure, and event-driven systems. Led architecture for systems handling 5M+ daily transactions with 99.9% availability. ## KEY ACHIEVEMENTS • 5M+ Daily Transactions handled with 99.9% availability • 15TB Daily Data Processing at scale • 40% Cost Reduction achieved across projects • Built agentic RAG with 87% F1 score for 2M documents • 60% reduction in AI hallucinations via MCP orchestration ## LEADERSHIP & TEAM MANAGEMENT • Managing 25+ engineers across US, India, and Europe • Technical advisor for C-suite executives on AI strategy • Led cross-functional teams (Engineering, Product, Sales) • Established AI Center of Excellence (15 members) • Mentored 30+ engineers to senior positions • Speaker at 10+ cloud/AI conferences globally [System Info: Profile loaded successfully]
LOADING TECHNICAL EXPERTISE... ═══════════════════════════════════════════════════════════════════════════════ ## AWS SERVICES ├── Compute: Lambda, EKS, EC2 ├── AI/ML: Bedrock, SageMaker, Personalize ├── Storage: S3, EBS, EFS ├── Database: DynamoDB, Aurora, RDS ├── Analytics: Kinesis, Glue, Athena, Redshift ├── Search: OpenSearch └── Workflow: Step Functions ## GCP SERVICES ├── AI/ML: Vertex AI, AutoML ├── Compute: Cloud Run, GKE, Compute Engine ├── Analytics: BigQuery, Dataflow, Pub/Sub ├── Storage: Cloud Storage, Firestore ├── Database: Cloud SQL, Spanner └── Workflow: Composer, Dataproc ## OCI SERVICES ├── AI/ML: Data Science, Generative AI ├── Compute: Functions, OKE, Compute ├── Database: Autonomous DB, MySQL HeatWave ├── Streaming: Streaming, Data Flow └── Storage: Object Storage ## AI/ML & GENAI TECHNOLOGIES ├── RAG Frameworks: LangChain, LlamaIndex, Semantic Kernel ├── Vector Databases: Pinecone, Weaviate, ChromaDB, Qdrant, FAISS ├── LLM Platforms: Claude (Anthropic), GPT-4 (OpenAI), Gemini (Google), Llama ├── Prompt Engineering: DSPy, Guidance, Few-shot/Chain-of-Thought prompting ├── ML Frameworks: PyTorch, TensorFlow, JAX, Hugging Face └── Embeddings: OpenAI Ada, Cohere, Sentence Transformers ## CLOUD-AGNOSTIC TOOLS ├── IaC: Terraform, Pulumi, CloudFormation, ARM Templates ├── Containers: Kubernetes, Docker, Helm, Istio ├── CI/CD: GitOps, Jenkins, GitHub Actions, ArgoCD ├── Monitoring: Prometheus, Grafana, ELK Stack, DataDog ├── ML Ops: MLflow, Kubeflow, Weights & Biases, Neptune └── Data: Apache Spark, Kafka, Databricks, Airflow, Snowflake, dbt [Skills matrix loaded: 100+ technologies]
WORK EXPERIENCE LOG ═══════════════════════════════════════════════════════════════════════════════ [2023-Present] ARCHITECT @ Tiger Analytics ├── Built agentic RAG with LangGraph + MCP + Bedrock (2M docs, 87% F1, autonomous agents) ├── Built MCP-based agent orchestration (30+ AI agents, 60% reduction in hallucinations) ├── Built ML model to optimize Docker images (100+ clients, 95% optimal in 30sec) ├── Achieved SOC2 compliance: KMS encryption, mTLS, CloudTrail + Splunk audit logging ├── Lead team of 25+ engineers across 3 geographic regions └── Crisis: Black Friday outage fixed in 2 hours with ProxySQL + circuit breakers [2020-2022] SENIOR CLOUD TECHNICAL ENGINEER @ Oracle Corporation ├── Migrated 50TB Oracle DB to OCI using GoldenGate (zero downtime, 30% cost reduction) ├── Built facial recognition pipeline on OKE with 4x A100s (500K images, bias mitigation) ├── Achieved SOC2 compliance: KMS encryption, mTLS, CloudTrail + Splunk audit logging └── Led OCI competitive analysis ($20M+ in wins) [2019-2020] TECHNICAL LEAD @ Paytm ├── Crisis: Black Friday outage (8x traffic spike) - fixed with ProxySQL in 2 hours ├── Built fraud detection with SageMaker + Kinesis (solved 40K WCU DynamoDB limit) └── Reduced cart abandonment 15% ($2M ARR) by optimizing API 800ms→200ms [2018-2019] SENIOR CONSULTANT @ Xebia IT Architects ├── Led AWS adoption for 10+ enterprises └── Built recommendation engine on AWS Personalize (3M users) [2016-2018] ASSOCIATE LEAD, TECHNOLOGY @ Nagarro Software ├── Built ML models for automotive simulations (60% compute reduction) └── Implemented predictive maintenance using IoT data [Experience loaded: 13+ years, 50+ projects]
MULTI-CLOUD SUCCESS STORIES ═══════════════════════════════════════════════════════════════════════════════ ## Agentic RAG Platform with LangGraph + MCP Stack: LangGraph, MCP Protocol, AWS Bedrock, OpenSearch Scale: 2M+ documents, autonomous agents self-correct Impact: 87% F1 score, 60% less hallucinations, 90% L1 query automation ─────────────────────────────────────────────────────────────────────────────── ## Real-time Analytics on GCP Stack: BigQuery, Dataflow, Pub/Sub, Vertex AI Scale: 5TB daily processing Impact: Sub-second queries, 100K predictions/sec ─────────────────────────────────────────────────────────────────────────────── ## Snowflake Data Mesh Stack: Snowflake, DBT, Airflow, Terraform Scale: Multi-region, 20+ teams Impact: 70% faster analytics, $2M annual savings ─────────────────────────────────────────────────────────────────────────────── ## OCI ML Platform for Manufacturing Stack: OCI Data Science, A100 GPUs, Autonomous DB Scale: 10TB daily processing Impact: 99.2% defect detection accuracy ─────────────────────────────────────────────────────────────────────────────── ## Fraud Detection System Stack: AWS SageMaker, Kinesis, Lambda, DynamoDB Scale: 5M transactions per second Impact: $12M+ fraud losses prevented [Projects loaded: 25+ GenAI deployments across clouds]
CERTIFICATION REGISTRY ═══════════════════════════════════════════════════════════════════════════════ ## AWS CERTIFICATIONS ├── [✓] AWS Solutions Architect - Professional ├── [✓] AWS Machine Learning - Specialty ├── [✓] AWS DevOps Engineer - Professional ├── [✓] AWS Well-Architected Partner ## GCP CERTIFICATIONS ├── [✓] Google Cloud Professional Cloud Architect ├── [✓] Google Cloud Professional ML Engineer ├── [✓] GCP Partner Specialization - ML ## ORACLE CERTIFICATIONS ├── [✓] Oracle Cloud Infrastructure Architect Professional └── [✓] OCI Cloud Excellence Implementor ## DATABRICKS CERTIFICATIONS ├── [✓] Databricks Data Engineer Professional ├── [✓] Databricks Machine Learning Engineer Professional └── [✓] Databricks Solutions Architect Champion ## ADDITIONAL CERTIFICATIONS ├── [✓] Multi-Cloud Migration Expert └── [✓] FinOps Certified Practitioner [Total Certifications: 10+ across AWS, GCP, OCI, Databricks]
EDUCATION RECORDS ═══════════════════════════════════════════════════════════════════════════════ ## M.S Data Science & Management Institution: IIM & IIT Indore Period: 2022-2024 Status: [COMPLETED] ## B.Tech Computer Science & Engineering Institution: JUET Period: 2008-2012 Status: [COMPLETED] ## THOUGHT LEADERSHIP ├── Semantic Protocol Layer Translation: Bidirectional protocol for AI agent interoperability (Research Paper) ├── Created secscan-cli: Open Source Multi Language Security Scanner └── Articles: "medium.com/@deoshankar" [Education loaded successfully]
CONTACT INFORMATION ═══════════════════════════════════════════════════════════════════════════════ ## CONNECT WITH ME Email: jha.deo1771@gmail.com Phone: +91-9891698505 Location: Noida, India LinkedIn: linkedin.com/in/deo-shankar GitHub: github.com/deosha ## BADGES & ACHIEVEMENTS ├── AWS Badges: 5+ ├── GCP Badges: 5+ └── OCI Badges: Multiple [Contact info loaded. Feel free to reach out!]
PERFORMANCE METRICS ═══════════════════════════════════════════════════════════════════════════════ ## IMPACT STATISTICS ┌─────────────────────────────────────────────────────────────┐ │ Daily Transactions: 5M+ with high availability │ │ System Availability: 99.9% across critical systems │ │ Data Processing: 15TB daily at scale │ │ Cost Reduction: 40% average achieved │ │ AI Accuracy: 87% F1 score on RAG platform │ │ Hallucination Reduced: 60% via MCP orchestration │ │ Teams Led: 100+ engineers trained │ │ Migrations Completed: 50+ enterprise cloud migrations │ └─────────────────────────────────────────────────────────────┘ ## REAL-TIME SYSTEM METRICS Current Production Systems Under Management ┌─────────────────────────────────────────────────────────────┐ │ LIVE SYSTEMS │ ├─────────────────────────────────────────────────────────────┤ │ Payment Gateway [████████████████████] 5.2M TPS │ │ RAG Pipeline [███████████████████░] 2M docs/day │ │ ML Inference [████████████████████] 100K req/sec │ │ Data Processing [██████████████████░░] 15TB daily │ └─────────────────────────────────────────────────────────────┘ ## TECHNOLOGY ADOPTION TIMELINE 2012: AWS, ETL (DMExpress), Oracle DB 2014: Spring Boot, Hibernate, JMS, SNMP 2016: Docker, Kubernetes, Terraform, GCP, Azure 2017: Microservices, Jenkins, CI/CD Pipelines 2018: Service Mesh, GitOps, Multi-cloud Strategy 2019: EKS, DynamoDB, Kinesis, SageMaker 2020: Oracle Cloud, GoldenGate, OKE 2021: Databricks, Snowflake, ML Platforms 2022: Vertex AI, Early GenAI exploration 2023: Bedrock, LangChain, Vector DBs, RAG 2024: LangGraph, Claude, Agentic AI Systems 2025: MCP Protocol (Nov 2024 release - Early Adopter) [Systems Operating at: 99.9% Availability | <1s P99 Latency]
CLOUD ARCHITECTURE PATTERNS ═══════════════════════════════════════════════════════════════════════════════ ## AGENTIC RAG ARCHITECTURE (LangGraph + MCP) ┌─────────────────────────────────────────────────────────────┐ │ User Queries │ └────────────────────────┬────────────────────────────────────┘ │ ┌────────────▼────────────┐ │ API Gateway │ │ (Rate Limiting, Auth) │ └────────────┬────────────┘ │ ┌────────────────▼────────────────┐ │ LangGraph Orchestrator │ │ (Self-correction loops, 87% F1)│ └────────┬───────────────┬────────┘ │ │ ┌────────────▼──────┐ ┌────▼──────────────┐ │ MCP Protocol │ │ AWS Bedrock │ │ (30+ AI Agents) │ │ Claude/Titan │ └────────────┬──────┘ └────┬──────────────┘ │ │ ┌───────▼───────────────▼───────┐ │ OpenSearch Vector DB │ │ (2M+ documents, 100ms) │ └───────────────┬───────────────┘ │ ┌──────────▼──────────┐ │ S3 Data Lake │ │ (Document Store) │ └────────────────────┘ ## EVENT-DRIVEN PAYMENT ARCHITECTURE (5M TPS) ┌───────────────────────────────────────────────────────────┐ │ Payment Requests │ └─────────────────────────┬──────────────────────────────────┘ │ ┌───────────▼───────────┐ │ API Gateway │ │ (ProxySQL, Circuit │ │ Breakers - 2hr fix) │ └───────────┬───────────┘ │ ┌─────────────────┴─────────────────┐ │ │ ┌───────▼────────┐ ┌──────────▼──────────┐ │ Kinesis/Kafka │ │ Lambda Functions │ │ Event Stream │ │ (CQRS Pattern) │ └───────┬────────┘ └──────────┬──────────┘ │ │ ┌───────▼────────────────────────────────────▼────────┐ │ DynamoDB (40K WCU) │ │ (Sharding strategy for scale) │ └──────────────────────┬──────────────────────────────┘ │ ┌──────────▼──────────┐ │ SageMaker Fraud │ │ Detection Model │ │ ($12M prevented) │ └────────────────────┘ ## COST-OPTIMIZED MULTI-CLOUD PATTERN ┌─────────────────────────────────────────────────────┐ │ Workload Distribution │ ├───────────────┬──────────────┬──────────────────────┤ │ Compute: EKS │ Analytics: │ AI/ML: Bedrock + │ │ (Spot 65% │ BigQuery │ Vertex AI │ │ savings) │ (Best TCO) │ (Model selection) │ └───────────────┴──────────────┴──────────────────────┘ │ │ │ ┌────▼────┐ ┌────▼────┐ ┌─────▼─────┐ │ AWS │────│ GCP │─────│ OCI │ └─────────┘ └─────────┘ └───────────┘ │ ┌───────────▼───────────┐ │ Unified Data Mesh │ │ (Snowflake/DBT) │ └───────────────────────┘ [Trade-offs: Eventual consistency for 10x throughput, 95% optimal in 30sec vs 100% in 5min] ## ADDITIONAL ARCHITECTURE PATTERNS 1. DOCKER IMAGE OPTIMIZATION PIPELINE ┌────────────────────────────────────────────────┐ │ Dockerfile Analysis Platform │ ├────────────────────────────────────────────────┤ │ Input: 3.5GB Images → Output: 350MB Images │ │ │ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ │ │Heuristic │→ │ Layer │→ │ Multi- │ │ │ │ Rules │ │ Merge │ │ Stage │ │ │ └──────────┘ └──────────┘ └──────────┘ │ │ │ │ Results: $300K/month saved | 95% adoption │ └────────────────────────────────────────────────┘ 2. MCP-BASED AGENT ORCHESTRATION ┌─────────────────┐ │ MCP Protocol │ │ Message Bus │ └────────┬─────────┘ │ ┌─────────────┼─────────────┬─────────────┐ ▼ ▼ ▼ ▼ ┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐ │ Data │ │ ML │ │ Code │ │ Ops │ │ Agents │ │ Agents │ │ Agents │ │ Agents │ └────────┘ └────────┘ └────────┘ └────────┘ 5 8 10 7 Hallucination: -60% | Tool Reuse: 80% | Speed: 3x 3. ZERO-DOWNTIME MIGRATION ARCHITECTURE ┌──────────────┐ GoldenGate ┌──────────────┐ │ On-Premise │==================>│ OCI │ │ Oracle DB │ Real-time CDC │ Autonomous │ │ 50TB │ │ Database │ └──────────────┘ └──────────────┘ ↓ ↓ $2M/year $1.4M/year 30% Cost Reduction 4. FRAUD DETECTION ML PIPELINE ┌─────────────────────────────────────────────────┐ │ Real-time Fraud Detection │ ├─────────────────────────────────────────────────┤ │ Transaction → Feature → XGBoost → Decision │ │ ↓ ↓ ↓ ↓ │ │ 5M TPS 47 dims <50ms 89% catch │ │ │ │ Sharded DynamoDB (10 tables @ 4K WCU each) │ │ SageMaker Endpoints (auto-scaling 3-100) │ └─────────────────────────────────────────────────┘ [More architectures available. Type specific project names for details.]
TECHNICAL PUBLICATIONS & THOUGHT LEADERSHIP ═══════════════════════════════════════════════════════════════════════════════ ## PHILOSOPHY "I believe in 'boring technology' for critical paths - proven patterns over bleeding edge. Perfect isn't always best: chose 95% optimal in 30sec over 100% optimal in 5min for Docker optimization. My reviews focus on: 'How does this fail?' before 'How does this work?' I choose trade-offs explicitly: DynamoDB over Aurora (10ms vs 50ms), eventual consistency over distributed transactions (10x simpler)." ## OPEN SOURCE PROJECTS ├── secscan-cli - Multi-language security vulnerability scanner │ └── 1.4k+ downloads | Sole creator └── Semantic Protocol Layer Translation - AI agent interoperability research └── Bidirectional translation for agent communication protocols (Under Review) ## TECHNICAL WRITING └── Medium Blog - Cloud architecture & AI/ML insights ## TECHNICAL LEADERSHIP ├── Architecture Reviews: Weekly design reviews preventing 3 major mistakes ├── Mentorship: Trained 15+ engineers across AWS, Kubernetes, and ML ├── Documentation: Created 20+ architectural decision records (ADRs) ├── Crisis Management: Led incident response for 5 P1 outages, established RCA └── Open Source: Maintain security tools with active community engagement [View all publications: medium.com/@deoshankar]
Connecting to deo@cloud-architect... The authenticity of host 'cloud-architect (10.0.13.37)' can't be established. ED25519 key fingerprint is SHA256:xGh3or1c1e/AwesomeCloudArchitect2024. This key is not known by any other names. Are you sure you want to continue connecting (yes/no/[fingerprint])? yes Welcome to Multi-Cloud Architecture System v13.0.0 * Documentation: https://github.com/deosha * Management: Multi-Cloud Control Plane Active * Support: 24/7 Architecture Support Enabled System information as of ${new Date().toLocaleString()} System load: 0.42, 0.69, 1.33 Processes: 313 Usage of /: 13.37% of 100TB Users logged in: 1 Memory usage: 42% IPv4 address: 10.0.13.37 Swap usage: 0% IPv6 address: ::1 => There are 3 cloud migrations pending review => 5 new GenAI models ready for deployment Last login: from your.browser.ip using portfolio [SSH session established. Type 'help' for available commands]
CONTAINER REGISTRY ═══════════════════════════════════════════════════════════════════════════════ CONTAINER ID IMAGE STATUS PORTS a7f8d9c2e4b1 genai-rag-service:latest Up 47 days 0.0.0.0:8080->8080/tcp b2c3d4e5f6a7 ml-inference-engine:v2.1 Up 23 days 0.0.0.0:5000->5000/tcp c8d9e0f1a2b3 data-pipeline-processor:3.0 Up 15 days 0.0.0.0:9090->9090/tcp d4e5f6a7b8c9 fraud-detection-model:prod Up 12 days 0.0.0.0:8000->8000/tcp e0f1a2b3c4d5 recommendation-api:latest Up 8 days 0.0.0.0:3000->3000/tcp f6a7b8c9d0e1 cost-optimizer:v1.5 Up 5 days 0.0.0.0:8888->8888/tcp a2b3c4d5e6f7 monitoring-dashboard:grafana Up 2 days 0.0.0.0:3001->3000/tcp b8c9d0e1f2a3 vector-database:qdrant Up 18 hours 0.0.0.0:6333->6333/tcp IMAGES REPOSITORY TAG SIZE CREATED genai-rag-service latest 2.3GB 2 months ago ml-inference-engine v2.1 1.8GB 1 month ago data-pipeline-processor 3.0 956MB 3 weeks ago fraud-detection-model prod 1.2GB 2 weeks ago recommendation-api latest 543MB 1 week ago [8 containers running | 15.7GB total disk usage]
KUBERNETES CLUSTER STATUS ═══════════════════════════════════════════════════════════════════════════════ ## DEPLOYMENTS NAME READY UP-TO-DATE AVAILABLE AGE genai-api-gateway 5/5 5 5 120d ml-inference-service 10/10 10 10 89d data-processing 8/8 8 8 67d fraud-detection 3/3 3 3 45d recommendation-engine 6/6 6 6 34d cost-analyzer 2/2 2 2 23d monitoring-stack 4/4 4 4 15d ## PODS NAME READY STATUS RESTARTS AGE genai-api-gateway-7d9b8c6f5-k8s2x 1/1 Running 0 2d genai-api-gateway-7d9b8c6f5-m3n4p 1/1 Running 0 2d genai-api-gateway-7d9b8c6f5-q5r6s 1/1 Running 0 2d ml-inference-service-6c7d8e9f0-a1b2c 1/1 Running 0 5d ml-inference-service-6c7d8e9f0-d3e4f 1/1 Running 0 5d data-processing-5b6c7d8e9-g5h6i 1/1 Running 0 1d fraud-detection-4a5b6c7d8-j7k8l 1/1 Running 0 8h ## SERVICES NAME TYPE CLUSTER-IP EXTERNAL-IP genai-api-gateway LoadBalancer 10.100.200.10 34.123.45.67 ml-inference ClusterIP 10.100.200.20data-processing ClusterIP 10.100.200.30 fraud-detection LoadBalancer 10.100.200.40 35.234.56.78 ## NODES NAME STATUS ROLES VERSION gke-cluster-1-default-pool-1a2b3c4d-5e6f Ready v1.27.3 gke-cluster-1-default-pool-1a2b3c4d-7g8h Ready v1.27.3 gke-cluster-1-gpu-pool-2b3c4d5e-6f7g Ready v1.27.3 [Cluster Health: Optimal | CPU: 42% | Memory: 58% | Pods: 38/110]
TERRAFORM INFRASTRUCTURE STATUS ═══════════════════════════════════════════════════════════════════════════════ Initializing Terraform... ✓ Terraform v1.5.7 initialized ## TERRAFORM PLAN Terraform will perform the following actions: # aws_eks_cluster.main will be created + resource "aws_eks_cluster" "main" { + arn = (known after apply) + certificate_authority = (known after apply) + cluster_id = (known after apply) + created_at = (known after apply) + endpoint = (known after apply) + id = (known after apply) + name = "genai-production-cluster" + platform_version = (known after apply) + role_arn = "arn:aws:iam::123456789:role/eks-cluster" + status = (known after apply) + version = "1.27" } # google_container_cluster.primary will be created + resource "google_container_cluster" "primary" { + cluster_ipv4_cidr = "10.0.0.0/14" + endpoint = (known after apply) + id = (known after apply) + location = "us-central1" + name = "ml-inference-cluster" + network = "projects/my-project/global/networks/main" + node_version = "1.27.3-gke.100" } # oci_containerengine_cluster.k8s_cluster will be created + resource "oci_containerengine_cluster" "k8s_cluster" { + compartment_id = "ocid1.compartment.oc1..xyz" + id = (known after apply) + kubernetes_version = "v1.27.2" + name = "data-processing-cluster" + vcn_id = "ocid1.vcn.oc1..abc" } Plan: 3 to add, 0 to change, 0 to destroy ## MANAGED RESOURCES ├── AWS: 127 resources across 5 regions ├── GCP: 89 resources across 3 regions ├── OCI: 45 resources across 2 regions └── Azure: 34 resources across 2 regions [Infrastructure as Code: 1000+ resources | 500K+ lines | 100% Automation]