I will build and deploy llm and ml apis with docker and AWS

This service is ideal if you want to build and deploy a startup-ready or production-grade distributed infrastructure for your ML or LLM application—from initial architecture and development to a fully deployed, scalable production system.

I Deploy in AWS, GCP, & Oracle Cloud According to your preferences and package selections.

It depends on your project's use case. If you have an existing workflow or preferred model, I can use that. Otherwise, I'll recommend the best open-source LLM—such as Llama, Mistral, DeepSeek, or Qwen—based on your business needs and performance requirements.

Yes, Based on your selected package, if you select Advance Setup you get auth service according to your preferences, like JWT, 0Auth2 etc on your ML production application.

Yeah, I use both. Kubernetes is primarily used when your application scales rapidly. It helps manage and orchestrate multiple Docker containers running your ML solution, allowing the service to automatically scale and handle increased traffic while maintaining reliability and performance.

For advanced cloud-native deployments, I use Go (Golang) because it's highly efficient at handling concurrency and multitasking. This enables your server to process multiple client requests and ML inference queries simultaneously, delivering high performance, low latency, and excellent scalability.

Yes! You'll get monitoring with Prometheus and Grafana tailored to your ML service. The Advanced package also includes a centralized dashboard to track model performance, latency, throughput, resource usage, error rates, and overall service health in one place.