Real results from AWS, GCP, Azure & NVIDIA certified DevOps, MLOps & GenAI projects
Client: E-commerce Platform (Series B Startup)
Challenge: Critical AI recommendation system outage affecting 100K+ users, revenue loss of $10K/hour
Solution: Leveraging AWS certified expertise, quickly diagnosed AI model serving bottleneck in EKS cluster, implemented emergency auto-scaling for ML inference workloads, and established comprehensive monitoring with CloudWatch and Prometheus to prevent future AI system failures.
AWS Certified Solution: Used AWS best practices for high-availability ML infrastructure
Client: AI Healthcare Startup
Challenge: Manual AI model deployment taking weeks, no model versioning, high cloud costs for ML workloads across multiple cloud providers
Solution: Built complete multi-cloud MLOps pipeline using certified expertise across AWS, GCP, and Azure. Implemented automated training, testing, and deployment with model registry, A/B testing, and comprehensive ML monitoring across all platforms.
Multi-Cloud Certified: Leveraged best practices from AWS, GCP & Azure certifications
Client: Enterprise SaaS Company
Challenge: Integrate LLM capabilities into existing product, handle 1M+ requests/day with cost optimization using AWS GenAI services
Solution: Designed and implemented scalable GenAI infrastructure using AWS Bedrock and certified GenAI developer expertise. Built auto-scaling architecture with intelligent caching, RAG systems, and cost optimization strategies.
AWS GenAI Developer Professional: Applied advanced GenAI patterns and best practices
Client: Financial Services Company
Challenge: Migrate AI workloads from on-premise to multi-cloud setup (AWS, Azure, GCP), ensure compliance and security
Solution: Planned and executed phased AI workload migration leveraging certifications across all three major cloud providers. Implemented full compliance, security, and disaster recovery for ML systems with Infrastructure as Code.
Azure DevOps 400 Certified: Used advanced DevOps practices for seamless migration
Client: AI Research Lab
Challenge: Optimize GPU utilization for deep learning training, reduce training costs, improve model iteration speed with NVIDIA infrastructure
Solution: Implemented distributed training infrastructure using NVIDIA certified expertise. Built GPU scheduling optimization, automated resource management for AI workloads, and multi-node training clusters.
NVIDIA NCA-AIIO Certified: Applied advanced GPU optimization and AI infrastructure patterns
Client: Fortune 500 Manufacturing Company
Challenge: Legacy deployment processes, no CI/CD, manual infrastructure management, multi-cloud complexity
Solution: Implemented comprehensive DevOps transformation using Azure DevOps certification expertise. Built CI/CD pipelines, Infrastructure as Code, automated testing, and monitoring across AWS, Azure, and GCP environments.
Azure DevOps 400 Certified: Implemented enterprise-grade DevOps practices and governance
Let's discuss how certified expertise across AWS, GCP, Azure & NVIDIA can solve your specific challenges and deliver measurable results.