Model Deployment
Curated Model Deployment tutorials.
Advanced RAG: Complete Guide 2026 – Beyond Basic Retrieval to Build Production-Grade Knowledge Bases
Basic RAG systems are easy to set up, but making them stable and effective in production is hard. This article dives deep into advanced RAG techniques: hybrid retrieval, reranking, multi-query decomposition, query routing, and systematic evaluation to improve RAG performance.
IntermediateFrom Demo to Production: A Practical Guide to Agent Harness Engineering
Agent Harness is the engineering infrastructure wrapped around the model, determining the success or failure of AI moving from demo to production. This article systematically covers the core concepts of Harness, the ETCLOVG seven-layer architecture, the five-tier memory system, dynamic workflows, and other key designs. Combined with practical cases like Claude Code, it provides a complete methodology covering context management, tool orchestration, and security governance. Suitable for developers and technical leaders who are bringing AI into real engineering.
AdvancedAI Agent Frameworks: LangChain, AutoGen & CrewAI for Production in 2025
AI agents go beyond chatbots—they use tools, maintain memory, plan multi-step tasks, and collaborate with other agents. This guide compares LangChain, LangGraph, AutoGen, and CrewAI for different use cases, covers reliable agent design patterns, tool calling best practices, memory architectures (short-term, long-term, episodic), handling errors and hallucinations, and deploying production agents with observability.
IntermediateThe Complete Guide to AI Agent Workflow Automation: From Zero to Production Deployment
Workflow automation is one of the highest-value scenarios for AI Agents. This article uses a 'daily competitive intelligence auto-collection + summary + push' pipeline as the main thread, explaining step by step how to use n8n for orchestration, Dify for AI processing, and MCP Server for tool integration to build a truly usable automation system.
AdvancedAI Agents in Production: Architecture Patterns and Reliability Engineering
AI agents—autonomous systems that use tools and make decisions to complete multi-step tasks—are moving into production at enterprise scale. This guide covers reliable agent architecture: tool design and error handling, state management for long-running agents, human-in-the-loop patterns, observability and debugging agents, graceful failure modes, security considerations, and testing strategies for non-deterministic systems.
AdvancedAI Audio Production and Sound Design: Tools for Modern Sound Designers
How sound designers and audio producers use AI for sound synthesis, texture generation, spatial audio, game audio, and post-production workflows—with tool comparisons and practical techniques.
AdvancedAI Canary Analysis
AI Canary Analysis: Safe Model Rollouts (2026): Route a new version to a small slice of traffic, compare against thresholds on operational + quality + safety metrics, and auto-promote or roll back. Includes mechanisms, Argo Rollouts/Flagger, per-region canaries, and fallback chains — putting automatic gates on fuzzy "better."
AdvancedDeploying AI Computer Vision in Production: From Training to Edge
A practical guide to building and deploying computer vision systems at production scale—covering object detection, image classification, video analytics, and edge deployment strategies.
IntermediateAutomating Data Science Workflows with AI: From EDA to Model Deployment
A comprehensive guide to automating the end-to-end data science workflow using AI tools—from automated exploratory data analysis and feature engineering to model selection, hyperparameter tuning, and production deployment.
AdvancedML Feature Store Architecture: Ensuring Consistency Between Online Serving and Offline Training Data
ML Feature Store Architecture (2026): Tackling training-serving skew—three sources of skew, offline/online dual storage with materialization synchronization, point-in-time join to eliminate time leakage. When you really need it (after being bitten), the convergence with vector stores in the LLM era, and practical tips for getting started with Feast.
IntermediateAI Short Video Mass Production Pipeline 2026: From Script to Final Cut in a Fully Automated Workflow
The core competitiveness of short videos lies in high-frequency updates. AI compresses the production time of a single video from 2 hours to 20 minutes. This article shares a complete AI short video workflow: viral script analysis → script generation → AI voiceover → video generation → post-production compositing, helping content teams establish a sustainable high-yield model.
IntermediateAzure OpenAI GPT-4 Deployment: Complete Guide for AI Applications 2026
Azure OpenAI GPT-4 Deployment: Complete Guide 2026 Overview Azure OpenAI GPT-4 Deployment provides enterprise-grade AI capabilities for deploying OpenAI models with Azure compliance. As one of the leading cloud AI platforms, it offers the reliability, scalability, and security that production applications demand.
IntermediateCelery for AI Applications: Async task processing for AI Guide 2026
Celery for AI Applications: async task processing for AI 2026 Introduction Use Celery to handle long-running AI tasks asynchronously in Python applications. This guide shows you how to effectively use Celery in your AI development workflow. Why Ce
AdvancedProduction Computer Vision with YOLO v11: Object Detection at Scale
Build production computer vision systems using YOLO v11 for object detection, including custom training, model optimization with TensorRT, edge deployment, and real-time video stream processing.
AdvancedDeploy Any GGUF Model on Ollama Local Server — Local development AI
Deploy Any GGUF Model on Ollama Local Server Overview Run Any GGUF Model directly on Ollama Local Server for local development AI. Local inference offers privacy, zero latency, and no ongoing API costs. **Specs**: CPU/GPU auto · Variable Installa
AdvancedDeploy Any ONNX Model on ONNX Runtime CrossPlatform — Cross-platform deployment
Deploy Any ONNX Model on ONNX Runtime CrossPlatform Overview Run Any ONNX Model directly on ONNX Runtime CrossPlatform for cross-platform deployment. Local inference offers privacy, zero latency, and no ongoing API costs. **Specs**: ONNX Runtime ·
AdvancedDeploy GGUF Models on LM Studio Desktop — No-code local AI GUI
Deploy GGUF Models on LM Studio Desktop Overview Run GGUF Models directly on LM Studio Desktop for no-code local AI GUI. Local inference offers privacy, zero latency, and no ongoing API costs. **Specs**: CPU/GPU · 8GB+ Installation ```bash Insta
AdvancedDeploy Llama 3.1 70B on vLLM Production Serving — High-throughput serving
Deploy Llama 3.1 70B on vLLM Production Serving Overview Run Llama 3.1 70B directly on vLLM Production Serving for high-throughput serving. Local inference offers privacy, zero latency, and no ongoing API costs. **Specs**: NVIDIA A100 · 80GB VRAM
AdvancedDeploy Mistral 7B on Intel Core Ultra Laptop — Laptop inference
Deploy Mistral 7B on Intel Core Ultra Laptop Overview Run Mistral 7B directly on Intel Core Ultra Laptop for laptop inference. Local inference offers privacy, zero latency, and no ongoing API costs. **Specs**: Intel NPU · 16-32GB Installation ``
AdvancedDeploy TinyLlama 1.1B on Raspberry Pi 5 — Home automation assistant
Deploy TinyLlama 1.1B on Raspberry Pi 5 Overview Run TinyLlama 1.1B directly on Raspberry Pi 5 for home automation assistant. Local inference offers privacy, zero latency, and no ongoing API costs. **Specs**: ARM CPU · 4GB RAM Installation ```ba
BeginnerDify Complete Tutorial 2026: How to build and deploy AI applications visually
Dify Complete Tutorial 2026 What is Dify? **Dify** is a powerful LLM app platform that enables you to build and deploy AI applications visually. It has become one of the most popular tools in the AI developer toolkit in 2026. Why Use Dify? - **Pr
IntermediateFastAPI + Anthropic: How to Build production FastAPI AI services (2026)
FastAPI + Anthropic Integration Guide 2026 Overview This guide shows you exactly how to build production FastAPI AI services using FastAPI and Anthropic. We cover setup, core integration, and production-ready patterns. Prerequisites - FastAPI env
IntermediateFastAPI vs LangServe: Side-by-Side Comparison
FastAPI vs LangServe Comparison (2026): Default to FastAPI—LangServe is in maintenance mode, with LangChain's deployment focus shifting to LangGraph Platform. Covers reasons for LangServe's decline, code examples of FastAPI serving any LLM stack directly, and when a stateful Agent is worth using a platform.
IntermediateGenerative AI Enterprise Strategy: From Pilots to Production at Scale
Strategic guide for enterprises deploying generative AI at scale, covering use case prioritization, build vs buy decisions, governance frameworks, ROI measurement, and organizational change management.
IntermediateHeyGen AI Avatar Videos for Enterprise: Scaling Training and Marketing Content
Enterprise guide to HeyGen AI avatar technology for corporate training, sales enablement, and marketing localization in 40+ languages with lip-sync and LMS integration.
BeginnerHow to Deploy an AI App to Vercel: Complete Guide for Developers 2026
How to Deploy an AI App to Vercel 2026 Introduction In this tutorial, you'll learn how to **Deploy an AI App to Vercel**. By the end, you'll have a working **deployed production AI app** that you can deploy and extend. **Prerequisites:** - Basic p
IntermediateHugging Face Inference API: Production Guide
Hugging Face Inference Production Guide (2026): First distinguish between two products—free serverless (for evaluation, cold start/rate limiting) vs Inference Endpoints (for production, dedicated GPU/SLA). HF wins on Hub long-tail models and private fine-tuned model hosting; mainstream LLMs are usually more cost-effective on specialized clouds. Includes cost threshold algorithm.
AdvancedLangChain LCEL: Advanced Patterns for Production AI Applications
LangChain Expression Language (LCEL) is the modern way to build composable LLM pipelines. This guide covers advanced LCEL patterns: parallel execution, streaming, dynamic routing, conditional chains, retry and fallback logic, tool use orchestration, and testing strategies. Includes production patterns for RAG applications, multi-step agents, and complex data transformation pipelines with real performance benchmarks.
IntermediateLlamaIndex Practical Guide: RAG Application Development from Beginner to Production
LlamaIndex is purpose-built for RAG applications, making it the go-to framework for building enterprise knowledge base Q&A systems. This article covers the core architecture, key differences from LangChain, and 5 complete code examples from document loading to production deployment.
AdvancedLlamaIndex Tutorial 2026: Build Production RAG Applications
Complete LlamaIndex tutorial 2026. Covers VectorStoreIndex, persistent Qdrant storage, chat engines, sub-question decomposition, semantic chunking, metadata filtering, and streaming.
AdvancedLLM Fine-Tuning for Production: LoRA, QLoRA & RLHF in 2025
Fine-tuning LLMs allows adapting powerful foundation models to specific domains without training from scratch. This guide covers LoRA and QLoRA for parameter-efficient fine-tuning, dataset preparation and quality filtering, instruction tuning format, RLHF and DPO for alignment, fine-tuning on consumer GPUs with quantization, evaluation with domain benchmarks, and deploying fine-tuned models with vLLM or TGI for production serving.
AdvancedReducing LLM Hallucinations: Practical Techniques for Production Applications
LLM hallucination—generating confident but false information—is the primary reliability challenge in production AI applications. This guide covers the root causes of hallucination, detection strategies (fact-checking layers, self-consistency checks, confidence calibration), mitigation techniques (RAG, constrained generation, chain-of-thought verification), and monitoring approaches for production systems. Includes benchmark data on hallucination rates across different model and technique combinations.
AdvancedLLM Inference Optimization: vLLM, TensorRT-LLM, and Serving at Scale
LLM inference optimization: vLLM, TensorRT-LLM, and serving at scale (2026). KV cache is the bottleneck—PagedAttention + continuous batching are the biggest throughput levers. Other techniques include vLLM vs TensorRT-LLM selection, quantization, speculative decoding, prefix caching, and choosing smaller models.
IntermediateMistral AI API Guide 2026: Mixtral, Mistral Large, and Edge Deployment
Comprehensive guide to Mistral AI API and models in 2026. Covers Mistral Large vs Mixtral model selection, API usage with Python and TypeScript, local deployment with Ollama, function calling, and building production applications with European data residency.
AdvancedML Metadata Management
ML Metadata Management Overview Tracking ML artifacts, lineage, and provenance with MLMD. This guide covers practical implementation for production ML systems. Why This Matters in MLOps Modern ML systems require rigorous operations practices: - *
BeginnerModal Complete Tutorial 2026: How to deploy Python AI code to cloud instantly
Modal Complete Tutorial 2026 What is Modal? **Modal** is a powerful cloud compute that enables you to deploy Python AI code to cloud instantly. It has become one of the most popular tools in the AI developer toolkit in 2026. Why Use Modal? - **Pr
IntermediateMulti-Provider AI Fallback: Production Guide
Multi-Vendor AI Fallback Production Architecture (2026): Centralized gateway strategy (LiteLLM config example), capability tier abstraction (apps call tiers not vendors), health routing + circuit breaking, signals for triggering vs. not triggering fallback. Covers pitfalls naive fallback misses: prompt portability, feature asymmetry, latency cliffs.
BeginnerOllama vs vLLM: Which is Better for local LLM deployment? (2026)
Ollama vs vLLM local LLM deployment deep comparison (2026): they solve different problems—Ollama is the simplest solution for single-machine/development (GGUF quantization, no NVIDIA GPU required), while vLLM is a production inference server for high concurrency (PagedAttention + continuous batching, requires CUDA). Includes real CLI/API code, throughput comparison, and the best practice of 'local Ollama for development, production vLLM for deployment'.
IntermediateBuild an AI Customer Support Agent with OpenAI Assistants API 2026
Step-by-step tutorial for building an AI customer support agent using the OpenAI Assistants API. Covers creating assistants, uploading knowledge base files, implementing function calling, managing threads, and deploying to production.
AdvancedOpenAI Assistants API in Production: Building Reliable AI Features for SaaS Applications
Production guide for OpenAI Assistants API — thread lifecycle management, function calling, file search, code interpreter integration, streaming responses, and cost optimization strategies for SaaS products.
IntermediatePrometheus + Grafana for AI Applications: Monitoring AI services Guide 2026
Prometheus + Grafana for AI Applications: monitoring AI services 2026 Introduction Set up comprehensive monitoring for LLM API costs, latency, and error rates. This guide shows you how to effectively use Prometheus + Grafana in your AI development
AdvancedPyTorch Lightning for Production Training: Best Practices and Advanced Features
Master PyTorch Lightning for production deep learning including multi-GPU training, mixed precision, gradient accumulation, callbacks, and integration with experiment tracking tools.
IntermediateRunway Gen-3 Alpha for Video Production: From Script to Final Cut
Comprehensive guide to using Runway Gen-3 Alpha for professional video production — text-to-video, image-to-video animation, style transfer, and camera control for cinematic movements.
IntermediateStable Diffusion 3.5 Local Deployment Complete Guide: Generate Unlimited Images for Free
SD 3.5 local deployment guide (2026): hardware table (Medium 8GB VRAM works), ComfyUI installation, model and text encoder placement (missing t5 is the #1 error), parameter tips (CFG 4-6), advanced roadmap for LoRA/ControlNet/batch API, and common error quick reference.
BeginnerTransformers.js vs ONNX Runtime: Which is Better for browser AI inference? (2026)
Transformers.js vs ONNX Runtime Web for browser-side AI inference (2026): Transformers.js is a high-level HF pipeline (which runs on ONNX Runtime under the hood), while ONNX Runtime Web is the low-level engine for custom models. Includes real JS code, WebGPU acceleration, and selection advice.
AdvancedVector Databases & RAG in Production: Pinecone, Weaviate & pgvector in 2025
Retrieval-Augmented Generation (RAG) is the dominant pattern for grounding LLMs with up-to-date knowledge. This guide covers vector database selection (Pinecone, Weaviate, Qdrant, pgvector), embedding model selection and optimization, chunking strategies for documents, hybrid search (vector + keyword), re-ranking, evaluating RAG quality, and deploying production RAG systems that stay accurate over time.
IntermediatevLLM High-Throughput Serving: Tutorial and Best Practices
vLLM High-Throughput Serving What is vLLM? vLLM is a framework for PagedAttention for GPU inference. It simplifies building AI applications by providing high-level abstractions over raw LLM APIs. **Best for**: serving Installation ```bash pip in