TensorBlue

AI Development Complete Guide

Everything you need to build production AI systems: from LLM fine-tuning to deployment and monitoring. A comprehensive resource hub curated by industry experts.

Artificial intelligence development has transformed from experimental research into a critical business capability. Organizations across industries are leveraging AI to automate processes, enhance decision-making, and create innovative products. This comprehensive guide serves as your complete resource for understanding and implementing production-grade AI systems.

Building successful AI applications requires expertise across multiple domains: data engineering, model development, infrastructure deployment, and continuous monitoring. Whether you're working with large language models (LLMs), computer vision systems, or predictive analytics, the principles of robust AI development remain consistent. This guide covers the entire lifecycle from initial concept to production deployment and ongoing optimization.

Our curated content clusters below organize resources by topic, making it easy to find exactly what you need. Each section includes detailed tutorials, best practices, case studies, and implementation guides. We've compiled insights from building AI systems that process millions of requests daily, maintain 99.9% uptime, and deliver measurable business value.

What You'll Learn

  • LLM fine-tuning techniques including LoRA, QLoRA, and full fine-tuning for GPT, Llama, and Mistral models
  • Retrieval Augmented Generation (RAG) implementation with vector databases and embedding strategies
  • Production deployment using Docker, Kubernetes, and cloud platforms for scalable AI infrastructure
  • MLOps best practices including CI/CD pipelines, model monitoring, and automated retraining
  • Industry-specific AI implementations for healthcare, finance, retail, and manufacturing
  • Advanced techniques including reinforcement learning, transfer learning, and federated learning

Content Clusters

LLMs & Fine-tuning

LLM Fine-tuning Complete Guide

Step-by-step guide to fine-tuning large language models for your use case.

LLM Quantization & Optimization

Reduce model size by 75% while maintaining 98% accuracy.

Prompt Engineering Best Practices

Master prompt engineering for GPT-4, Claude, and other LLMs.

LLM Fine-tuning Services

Professional fine-tuning services with 96%+ accuracy guarantee.

RAG & Knowledge Systems

RAG Implementation Guide

Build production RAG systems with 90%+ answer accuracy.

Vector Database Comparison

Complete comparison: Pinecone, Weaviate, Qdrant, Milvus, ChromaDB.

RAG as a Service

Fully managed RAG solutions for enterprises.

MLOps & Production Deployment

MLOps Best Practices

CI/CD, monitoring, and production deployment for ML systems.

AI Model Deployment Guide

Deploy AI models with Docker, Kubernetes, 99.9% uptime.

Model Monitoring & Drift Detection

Monitor performance, detect drift, maintain accuracy in production.

AI for Specific Industries

AI for Healthcare

HIPAA-compliant AI implementation guide.

AI for Finance

Fraud detection, risk assessment, algorithmic trading.

AI for Manufacturing

Quality control, predictive maintenance, optimization.

AI for Retail

Personalization, demand forecasting, inventory optimization.

AI for Education

Personalized learning, automated grading, student analytics.

AI for Legal

Contract analysis, legal research, compliance automation.

Advanced AI Techniques

Reinforcement Learning Guide

DQN, PPO, A3C for robotics, games, and optimization.

Transfer Learning

Build models 10-50x faster with pre-trained models.

Federated Learning

Privacy-preserving ML for healthcare and finance.

Multi-Modal Learning

Combine vision, text, and audio for advanced AI.

Our AI Development Services

LLM Fine-tuning

Custom model training with 96%+ accuracy guarantee.

RAG as a Service

Fully managed retrieval augmented generation solutions.

AI Chatbot Development

Intelligent chatbots with 4-10x faster delivery.

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