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Connect with Us to Learn More - hello@remoder.com
Signed in as:
filler@godaddy.com
Explore in-depth resources, technical guides, and project whitepapers created by Remoder β designed to help engineers understand, deploy, and scale AI systems with confidence.
From hands-on labs to AI infrastructure blueprints, each document offers practical insights and real-world examples straight from our projects.
π§ βοΈ Re-modernizing Engineering for the AI Era β where human brilliance meets machine intelligence. π‘
π Simple Python π§ LLM App [Lab 1 β Project 1, V1]
This project walks you through building and deploying your first AI inference API using FastAPI, DistilGPT2, and Docker.
Itβs part of Remoderβs AI Engineer Upskilling Program, designed to help engineers understand how to run LLMs locally and serve them through an API.
Youβll learn how to:
A perfect starting point for anyone learning AI systems engineering, this lab transforms a simple script into a production-ready AI API.
π‘οΈ Secured Simple Python LLM App [Lab 1 β Project 1, V2]
This updated version of Project 1 enhances the original Simple Python LLM App with a strong focus on security, efficiency, and responsible AI engineering.
Built as part of Remoderβs AI Engineer Upskilling Program, this version goes beyond functionality β it teaches how to secure your AI Agents end-to-end.
Youβll learn how to:
This version marks a shift from βjust running AIβ to deploying it responsibly and securely, laying the foundation for production-grade AI systems.
DOCKERIZED AI INFERENCE API : NGINX + OLLAMA
[ Lab 1 β Project 2, V1 ]
This document walks through the complete setup of a Dockerized AI inference system built using Ollama for running local LLMs and Nginx as a secure reverse proxy layer.
Youβll learn how to:
This guide provides a production-style blueprint for hosting AI APIs securely and efficiently β a perfect foundation for engineers learning AI systems deployment and infrastructure automation.
π DOCKERIZED AI INFERENCE API: NGINX + OLLAMA
This upgraded version of Project 2 takes the original Ollama + Nginx AI API and transforms it into a fully secured, production-ready deployment.
Itβs designed to teach engineers how to build responsible and secure AI workloads β combining AI inference, network security, and cloud portability.
Youβll learn how to:
This document is your blueprint for serving AI models securely at scale, built for real-world DevOps and AI infrastructure environments.
π³ DOCKERIZING AI β THE FOUNDATION FOR AI + DEVOPS, v1
This document explores how Docker revolutionizes AI development by eliminating environment issues and enabling reproducible, scalable machine learning workflows.
It covers image isolation, consistent deployments, and security fundamentals, showing how Docker turns AI code into portable, production-ready artifacts.
Youβll learn how to:
βοΈ Docker Essentials for AI & DevOps Engineers v2
This version refines the fundamentals β turning theory into practice. It teaches engineers how to use Docker and Docker Compose to containerize AI workloads like LLMs, Vector DBs, and APIs in real-world environments.
Youβll learn how to:
π§ PROJECT 3: AI POWER STACK β FROM MODELS TO MONITORING
β’ π See how FastAPI acts as the brainβs interface for the AI Agent.
β’ π§ Watch Ollama generate real responses using local LLMs.
β’ π§Ύ Explore how ChromaDB stores and retrieves vector-based knowledge.
β’ π Learn how Prometheus & Grafana visualize metrics and performance in real-time.
⒠𧩠Understand how Docker Compose orchestrates all components with one command.
π¦ Ollama CLI
Essential guide and best practices when working with OLLAMA or OLLAMA-CLI
vLLM vs π¦Ollama
Choosing an LLM is only part of the problem.
How you run inference is an infrastructure decision.
This guide focuses on:
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