Java-first AI engineering
Learn to build real AI applications using Spring Boot and Spring AI instead of generic Python-only tutorials.
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SOFTWARE DEVELOPMENT · İRƏLI SƏVİYYƏ
AI for Java Developers
Master AI engineering with Java and Spring AI — from prompt engineering and RAG to agents, production resilience, and building neural networks and LLMs from scratch.
İrəli24 həftə180 saatHibrid
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KURSUN ÜSTÜNLÜKLƏRİ
From API calls to first principles
Go beyond using LLMs — understand and build the math, neural networks, and transformer architecture behind them.
Production-grade architecture
Cover resilience, security, observability, and scalability patterns needed for real enterprise AI systems.
SƏVİYYƏNİZ
BaşlanğıcOrtaİrəliEkspert
NƏ EDƏ BİLƏCƏKSİNİZ
Bu kursun sonunda sahib olacağınız bacarıqlar.
- Build production-grade AI applications in Java using Spring Boot and Spring AI
- Design and implement RAG pipelines with embeddings, vector databases, and reranking
- Build tool-calling agents and orchestrate multi-agent workflows using MCP
- Architect resilient, secure, and observable AI systems for enterprise use
- Apply model routing strategies across local, open-source, and multi-provider LLMs
- Understand and implement the mathematics behind neural networks and transformers
- Build a neural network and a small GPT-style language model from scratch
- Fine-tune, quantize, and deploy open-source models, including multimodal use cases
QISALDILMIŞ SYLLABUS
Dərslik oxumadan strukturu görün.
Modullar sürətli baxış üçün yığcam qalır. Mövzularına baxmaq üçün istənilən modulu açın.
01Introduction to AI & LLM Engineering6 dərs+
AI, Machine Learning & Generative AI Foundations
What Are LLMs? Models, Providers & Tokens
Context Windows, Temperature & Sampling
Prompt Engineering & Message Roles
Prompt Templates & Structured Output
Spring AI, ChatClient, Memory & Streaming
02Building AI Applications with Java6 dərs+
Spring Boot + Spring AI Application Setup
Structured AI Responses & Conversation Management
Tool Calling and Tool Design
External APIs as Read & Write Tools
Embeddings, Semantic Search & Vector Databases
PostgreSQL + pgvector and Basic RAG
03RAG & AI Data Engineering6 dərs+
RAG Architecture & Knowledge Ingestion
Website, HTML, PDF & Database Processing
Data Cleaning, Chunking & Chunk Overlap
Metadata, Embedding Pipelines & Vector Search
Hybrid Search, Query Rewriting & Reranking
Citations, Knowledge Synchronization & RAG Evaluation
04MCP, Agents & AI Workflows6 dərs+
Model Context Protocol Architecture, Client & Server
MCP Tools, Resources & Enterprise System Integration
What Is an AI Agent? Agent vs Chat vs Workflow
Agent State, Memory, Planning & Reasoning
Agent Loops, Termination & Multi-Agent Communication
Deterministic vs Agentic Workflows & Human-in-the-Loop
05AI System Architecture & Resilience6 dərs+
AI-Native Architecture, Orchestration & Model Gateway/Router
Event-Driven AI, Kafka & Microservice Integration
Sync vs Async and Multi-Tenant AI Platforms
LLM Failure Modes, Timeouts, Retries & Circuit Breakers
Caching, Semantic Caching & Rate Limiting
Reactive AI, Backpressure & Cost/Token Optimization
06AI Security, Evaluation & Observability6 dərs+
AI Threat Model: Prompt Injection, Jailbreaking & RAG Poisoning
Tool Abuse, Authorization, Least Privilege & Data Privacy
Guardrails, Input/Output Validation & Secure Agent Design
AI Evals: Groundedness, Relevance, Correctness & Hallucination Detection
Tool-Calling & Agent Evaluation, Regression Testing
AI Tracing with OpenTelemetry, Micrometer, Prometheus & Grafana
07Local Models & Multi-Model Architecture5 dərs+
Open-Source & Local LLMs with Ollama
GPU vs CPU Inference and Quantization
Small vs Large Language Models & Reasoning Models
Model Selection, Routing & Multi-Provider Architecture
Cost-, Latency- and Privacy-Based Routing
08Mathematics & Machine Learning Foundations5 dərs+
Linear Algebra: Vectors, Matrices & Cosine Similarity
Probability, Statistics & Distributions
Calculus & Optimization: Gradients and Gradient Descent
Supervised & Unsupervised Learning, Regression & Classification
Overfitting, Regularization & Model Evaluation Metrics
09Neural Networks from Scratch6 dərs+
Artificial Neurons, Weights, Bias & Activation Functions
Forward Propagation and Loss Functions
Backpropagation and the Chain Rule in Neural Networks
Weight Initialization and Optimization
Building a Neural Network from Scratch
Training with PyTorch and GPU Training
10Building an LLM from Scratch6 dərs+
Tokenization, Vocabulary, Token IDs & Embeddings
Self-Attention: Query, Key, Value & Attention Scores
Multi-Head Attention and Transformer Blocks
Decoder-Only Transformers and Next-Token Prediction
Building a Tokenizer, Attention & a Small GPT-Style Model
Training the Model and Generating Text
11Fine-Tuning, Computer Vision & Multimodal AI5 dərs+
Pretraining, Instruction Tuning & Post-Training
Supervised Fine-Tuning, LoRA & Quantization
Fine-Tuning Open Models, Serving & Deployment
Computer Vision: CNNs, Image Classification & Vision Transformers
Multimodal AI: Vision-Language Models and Multimodal RAG
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Dərs saatları
180 saat
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