From Java Dev to AI Engineer: Spring AI Fast Track [8/2025, ENG]
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Files
| 1 - Spring AI – Say Hello to AI in Spring Boot | |||
| 1 -Course Introduction.mp4 | 177.42 MB | ||
| 1 -Course Introduction.vtt | 13.11 KB | ||
| 10 -Working with Multiple Chat Models in Spring AI.mp4 | 91.14 MB | ||
| 10 -Working with Multiple Chat Models in Spring AI.vtt | 12.76 KB | ||
| 2 - Details of Source Code, PDF Content & other instructions for the course.html | 1.77 KB | ||
| 2 -From Java Dev to AI Engineer- Spring AI Fast Track.pdf | 28.81 MB | ||
| 2 -What is Spring AI Framework.mp4 | 51.94 MB | ||
| 2 -What is Spring AI Framework.vtt | 6.96 KB | ||
| 3 -Hello World Spring AI app with OpenAI - Part 1.mp4 | 91.43 MB | ||
| 3 -Hello World Spring AI app with OpenAI - Part 1.vtt | 14.74 KB | ||
| 4 -Hello World Spring AI app with OpenAI - Part 2.mp4 | 62.64 MB | ||
| 4 -Hello World Spring AI app with OpenAI - Part 2.vtt | 14.01 KB | ||
| 5 -Deep dive on ChatModel and ChatClient.mp4 | 48.24 MB | ||
| 5 -Deep dive on ChatModel and ChatClient.vtt | 7.71 KB | ||
| 6 -Building a “Hello World” App with Spring AI and Ollama.mp4 | 56.65 MB | ||
| 6 -Building a “Hello World” App with Spring AI and Ollama.vtt | 12.67 KB | ||
| 7 -Building a “Hello World” App with Spring AI and Docker.mp4 | 62.18 MB | ||
| 7 -Building a “Hello World” App with Spring AI and Docker.vtt | 11.43 KB | ||
| 8 -Building a “Hello World” App with Spring AI and AWS Bedrock - Part 1.mp4 | 104.59 MB | ||
| 8 -Building a “Hello World” App with Spring AI and AWS Bedrock - Part 1.vtt | 14.33 KB | ||
| 9 -Building a “Hello World” App with Spring AI and AWS Bedrock - Part 2.mp4 | 71.71 MB | ||
| 9 -Building a “Hello World” App with Spring AI and AWS Bedrock - Part 2.vtt | 11.46 KB | ||
| 10 - Transcription, Speech, and Image Generation using Spring AI | |||
| 1 -From Voice to Text - Using Spring AI for Transcription - Part 1.mp4 | 57.93 MB | ||
| 1 -From Voice to Text - Using Spring AI for Transcription - Part 1.vtt | 10.16 KB | ||
| 2 -From Voice to Text - Using Spring AI for Transcription - Part 2.mp4 | 82.88 MB | ||
| 2 -From Voice to Text - Using Spring AI for Transcription - Part 2.vtt | 12.19 KB | ||
| 3 -From Text to Speech - Generating Voices with SpeechModel.mp4 | 80.3 MB | ||
| 3 -From Text to Speech - Generating Voices with SpeechModel.vtt | 11.89 KB | ||
| 4 -From Prompt to Picture - Image Generation with ImageModel.mp4 | 89.02 MB | ||
| 4 -From Prompt to Picture - Image Generation with ImageModel.vtt | 13.24 KB | ||
| 11 - Thank You & Congratulations | |||
| 1 -Thank You & Congratulations.mp4 | 19.48 MB | ||
| 1 -Thank You & Congratulations.vtt | 1.95 KB | ||
| 2 - Spring AI Essentials - Prompts, Advisors, and Structured Responses | |||
| 1 -Understanding Message Roles in LLMs - Theory.mp4 | 58.58 MB | ||
| 1 -Understanding Message Roles in LLMs - Theory.vtt | 8.04 KB | ||
| 10 -Understanding ChatOptions in Spring AI.mp4 | 89.12 MB | ||
| 10 -Understanding ChatOptions in Spring AI.vtt | 12.66 KB | ||
| 11 -Configuring ChatOptions in Spring AI.mp4 | 59.21 MB | ||
| 11 -Configuring ChatOptions in Spring AI.vtt | 8.05 KB | ||
| 12 -Spring AI ChatClient – Response Types Explained.mp4 | 50.74 MB | ||
| 12 -Spring AI ChatClient – Response Types Explained.vtt | 7.7 KB | ||
| 13 -Streaming AI Responses in Real-Time using stream() Method.mp4 | 33.2 MB | ||
| 13 -Streaming AI Responses in Real-Time using stream() Method.vtt | 5.68 KB | ||
| 14 -From Text to Types - Mastering Structured Output in Spring AI.mp4 | 44.66 MB | ||
| 14 -From Text to Types - Mastering Structured Output in Spring AI.vtt | 7.69 KB | ||
| 15 -From AI Text to Java Objects - Spring AI Structured Output Demo.mp4 | 66.58 MB | ||
| 15 -From AI Text to Java Objects - Spring AI Structured Output Demo.vtt | 9.24 KB | ||
| 16 -Using Bean, List, and Map Output Converters in Spring AI.mp4 | 50.26 MB | ||
| 16 -Using Bean, List, and Map Output Converters in Spring AI.vtt | 7.06 KB | ||
| 17 -Mapping AI Response to ListPOJO using ParameterizedTypeReference.mp4 | 40.64 MB | ||
| 17 -Mapping AI Response to ListPOJO using ParameterizedTypeReference.vtt | 4.93 KB | ||
| 2 -Understanding Message Roles in LLMs - Demo.mp4 | 72.82 MB | ||
| 2 -Understanding Message Roles in LLMs - Demo.vtt | 12.36 KB | ||
| 3 -What Are Defaults in Spring AI.mp4 | 90.9 MB | ||
| 3 -What Are Defaults in Spring AI.vtt | 12.82 KB | ||
| 4 -Using Prompt Templates in Spring AI.mp4 | 105.61 MB | ||
| 4 -Using Prompt Templates in Spring AI.vtt | 16.61 KB | ||
| 5 -Using Prompt Stuffing technique.mp4 | 84.87 MB | ||
| 5 -Using Prompt Stuffing technique.vtt | 11.92 KB | ||
| 6 -Why Prompt Stuffing Isn’t Meant for Big Data.mp4 | 31.78 MB | ||
| 6 -Why Prompt Stuffing Isn’t Meant for Big Data.vtt | 7.93 KB | ||
| 7 -Understanding Advisors in Spring AI Workflows.mp4 | 38.43 MB | ||
| 7 -Understanding Advisors in Spring AI Workflows.vtt | 6.19 KB | ||
| 8 -Spring AI Built-in Advisors - Plug and Play Intelligence.mp4 | 100.82 MB | ||
| 8 -Spring AI Built-in Advisors - Plug and Play Intelligence.vtt | 14.21 KB | ||
| 9 -Custom Advisors in Spring AI - Make the AI Work Your Way.mp4 | 87.06 MB | ||
| 9 -Custom Advisors in Spring AI - Make the AI Work Your Way.vtt | 12.56 KB | ||
| 3 - Foundations of Generative AI and LLMs | |||
| 1 -Tracing the Roots - How We Reached Generative AI.mp4 | 32.6 MB | ||
| 1 -Tracing the Roots - How We Reached Generative AI.vtt | 6.04 KB | ||
| 10 -How Embedding Vectors calculated.mp4 | 91.99 MB | ||
| 10 -How Embedding Vectors calculated.vtt | 14.36 KB | ||
| 11 -What are Static Embeddings.mp4 | 44.52 MB | ||
| 11 -What are Static Embeddings.vtt | 7.4 KB | ||
| 12 -Positional Embeddings - How AI Understands Word Order.mp4 | 32.2 MB | ||
| 12 -Positional Embeddings - How AI Understands Word Order.vtt | 5.48 KB | ||
| 13 -The Magic of Attention - How AI Understands Context.mp4 | 76.57 MB | ||
| 13 -The Magic of Attention - How AI Understands Context.vtt | 9.3 KB | ||
| 2 -Meet the Gen AI Family - AI, ML, DL, and Beyond.mp4 | 99.21 MB | ||
| 2 -Meet the Gen AI Family - AI, ML, DL, and Beyond.vtt | 13.67 KB | ||
| 3 -Funny memes of AI.mp4 | 26.89 MB | ||
| 3 -Funny memes of AI.vtt | 6.87 KB | ||
| 4 -Types of Generative AI Models.mp4 | 25.04 MB | ||
| 4 -Types of Generative AI Models.vtt | 4.3 KB | ||
| 5 -Introduction to Large Language Models - The Text Experts.mp4 | 88.52 MB | ||
| 5 -Introduction to Large Language Models - The Text Experts.vtt | 12.54 KB | ||
| 6 -Core job of an LLM - Guessing next word.mp4 | 56.04 MB | ||
| 6 -Core job of an LLM - Guessing next word.vtt | 9.28 KB | ||
| 7 -The concept of Tokens & Token IDs in LLM.mp4 | 56.64 MB | ||
| 7 -The concept of Tokens & Token IDs in LLM.vtt | 10.27 KB | ||
| 8 -Inside an LLM’s Dictionary - Understanding Model Vocabulary.mp4 | 59.43 MB | ||
| 8 -Inside an LLM’s Dictionary - Understanding Model Vocabulary.vtt | 10.56 KB | ||
| 9 -Embeddings & Vectors - A way to represent meaning.mp4 | 61.04 MB | ||
| 9 -Embeddings & Vectors - A way to represent meaning.vtt | 9.11 KB | ||
| 4 - Teaching LLMs to Remember - The Power of Chat Memory in Spring AI | |||
| 1 -LLMs Don’t Remember You – The Stateless Nature of AI Models.mp4 | 42.47 MB | ||
| 1 -LLMs Don’t Remember You – The Stateless Nature of AI Models.vtt | 7.54 KB | ||
| 2 -LLMs Forget Everything — ChatMemory to the Rescue!.mp4 | 71.27 MB | ||
| 2 -LLMs Forget Everything — ChatMemory to the Rescue!.vtt | 10.07 KB | ||
| 3 -Making ChatClient ‘Remember’ – Spring AI’s Memory Advisors.mp4 | 56.25 MB | ||
| 3 -Making ChatClient ‘Remember’ – Spring AI’s Memory Advisors.vtt | 7.97 KB | ||
| 4 -From Stateless to Stateful - Spring AI Memory in Action.mp4 | 71.76 MB | ||
| 4 -From Stateless to Stateful - Spring AI Memory in Action.vtt | 11.55 KB | ||
| 5 -Per-User Memory in LLMs – Thanks to CONVERSATION_ID!.mp4 | 40.15 MB | ||
| 5 -Per-User Memory in LLMs – Thanks to CONVERSATION_ID!.vtt | 6.72 KB | ||
| 6 -Chat Memory That Lasts – Spring AI with JDBC-Based Persistence.mp4 | 105.58 MB | ||
| 6 -Chat Memory That Lasts – Spring AI with JDBC-Based Persistence.vtt | 16.08 KB | ||
| 7 -Using maxMessages to Limit Chat History in Spring AI.mp4 | 44.23 MB | ||
| 7 -Using maxMessages to Limit Chat History in Spring AI.vtt | 6.46 KB | ||
| 8 -Avoiding Token Overload - Use maxMessages with Context Window size in Mind.mp4 | 66.19 MB | ||
| 8 -Avoiding Token Overload - Use maxMessages with Context Window size in Mind.vtt | 10.15 KB | ||
| 5 - The Art of Talking to Documents – RAG Unleashed | |||
| 1 -From Prompt Stuffing to RAG – A Better Way to Feed LLMs.mp4 | 35.14 MB | ||
| 1 -From Prompt Stuffing to RAG – A Better Way to Feed LLMs.vtt | 6.77 KB | ||
| 10 -RAG Made Easy - RetrievalAugmentationAdvisor in Action.mp4 | 49.22 MB | ||
| 10 -RAG Made Easy - RetrievalAugmentationAdvisor in Action.vtt | 6.44 KB | ||
| 11 -Who calculates the embedding values of the documents.mp4 | 84.99 MB | ||
| 11 -Who calculates the embedding values of the documents.vtt | 11.77 KB | ||
| 2 -Understanding the RAG flow.mp4 | 42.14 MB | ||
| 2 -Understanding the RAG flow.vtt | 8.4 KB | ||
| 3 -Meet the Vector Database - Storing Meaning, Not Just Data.mp4 | 57.88 MB | ||
| 3 -Meet the Vector Database - Storing Meaning, Not Just Data.vtt | 9.2 KB | ||
| 4 -Setting Up Qdrant Vector Store with Spring Boot Using Docker Compose.mp4 | 67.89 MB | ||
| 4 -Setting Up Qdrant Vector Store with Spring Boot Using Docker Compose.vtt | 10.48 KB | ||
| 5 -Demo of RAG using random text data - Part 1.mp4 | 87.96 MB | ||
| 5 -Demo of RAG using random text data - Part 1.vtt | 11.88 KB | ||
| 6 -Demo of RAG using random text data - Part 2.mp4 | 81.48 MB | ||
| 6 -Demo of RAG using random text data - Part 2.vtt | 11.76 KB | ||
| 7 -Demo of RAG using random text data - Part 3.mp4 | 45.05 MB | ||
| 7 -Demo of RAG using random text data - Part 3.vtt | 7.12 KB | ||
| 8 -Let's talk to documents using RAG.mp4 | 110.96 MB | ||
| 8 -Let's talk to documents using RAG.vtt | 16.08 KB | ||
| 9 -Breaking Down Big Documents - The Art of Chunking for LLMs.mp4 | 43.7 MB | ||
| 9 -Breaking Down Big Documents - The Art of Chunking for LLMs.vtt | 6.5 KB | ||
| 6 - Tool Calling in Action - Giving LLMs the Power to Do Things | |||
| 1 -Introduction to Tool Calling in AI.mp4 | 53.49 MB | ||
| 1 -Introduction to Tool Calling in AI.vtt | 7.33 KB | ||
| 10 -Generative AI’s Journey - From Static Chat to Dynamic Intelligence.mp4 | 71.12 MB | ||
| 10 -Generative AI’s Journey - From Static Chat to Dynamic Intelligence.vtt | 9.05 KB | ||
| 2 -LLMs vs Reality - The Time Question They Can’t Answer.mp4 | 43.29 MB | ||
| 2 -LLMs vs Reality - The Time Question They Can’t Answer.vtt | 7.98 KB | ||
| 3 -Tool Calling in Action - Teaching LLMs to Tell the Time.mp4 | 80.87 MB | ||
| 3 -Tool Calling in Action - Teaching LLMs to Tell the Time.vtt | 13.31 KB | ||
| 4 -How the Tools get called by LLM internally.mp4 | 100.05 MB | ||
| 4 -How the Tools get called by LLM internally.vtt | 14.87 KB | ||
| 5 -Letting LLMs Talk to Databases - A Tool Calling Demo - Part 1.mp4 | 85.09 MB | ||
| 5 -Letting LLMs Talk to Databases - A Tool Calling Demo - Part 1.vtt | 13.19 KB | ||
| 6 -Letting LLMs Talk to Databases - A Tool Calling Demo - Part 2.mp4 | 91.34 MB | ||
| 6 -Letting LLMs Talk to Databases - A Tool Calling Demo - Part 2.vtt | 12.88 KB | ||
| 7 -Letting LLMs Talk to Databases - A Tool Calling Demo - Part 3.mp4 | 72.9 MB | ||
| 7 -Letting LLMs Talk to Databases - A Tool Calling Demo - Part 3.vtt | 10.66 KB | ||
| 8 -Bypass the Model - Returning Tool Results Directly to the User.mp4 | 50.3 MB | ||
| 8 -Bypass the Model - Returning Tool Results Directly to the User.vtt | 6.82 KB | ||
| 9 -Graceful Failure - Customizing Tool Error Responses in Spring AI.mp4 | 43.35 MB | ||
| 9 -Graceful Failure - Customizing Tool Error Responses in Spring AI.vtt | 6.42 KB | ||
| 7 - Mastering Model Context Protocol (MCP) | |||
| 1 -The Missing Link in AI Apps - Understanding the Need for MCP.mp4 | 34.89 MB | ||
| 1 -The Missing Link in AI Apps - Understanding the Need for MCP.vtt | 4.61 KB | ||
| 10 -Building an MCP Server using remote invocation.mp4 | 67.5 MB | ||
| 10 -Building an MCP Server using remote invocation.vtt | 11.48 KB | ||
| 2 -Inside MCP - Understanding the Architecture of Model Context Protocol.mp4 | 77.49 MB | ||
| 2 -Inside MCP - Understanding the Architecture of Model Context Protocol.vtt | 12.2 KB | ||
| 3 -Behind the Scenes of MCP Communication - Built-in Transport Logic.mp4 | 66.1 MB | ||
| 3 -Behind the Scenes of MCP Communication - Built-in Transport Logic.vtt | 11.78 KB | ||
| 4 -Building an MCP Client with Spring AI.mp4 | 107.57 MB | ||
| 4 -Building an MCP Client with Spring AI.vtt | 17.76 KB | ||
| 5 -Demo of MCP Client and MCP Server integration.mp4 | 30.28 MB | ||
| 5 -Demo of MCP Client and MCP Server integration.vtt | 6.3 KB | ||
| 6 -Meet the MCP Inspector - The X-Ray Tool of MCP Server.mp4 | 54.29 MB | ||
| 6 -Meet the MCP Inspector - The X-Ray Tool of MCP Server.vtt | 9.52 KB | ||
| 7 -Demo of integration with GitHub MCP Server.mp4 | 85.79 MB | ||
| 7 -Demo of integration with GitHub MCP Server.vtt | 14.76 KB | ||
| 8 -Building an MCP Server using STDIO - Part 1.mp4 | 111.15 MB | ||
| 8 -Building an MCP Server using STDIO - Part 1.vtt | 17.47 KB | ||
| 9 -Building an MCP Server using STDIO - Part 2.mp4 | 75.98 MB | ||
| 9 -Building an MCP Server using STDIO - Part 2.vtt | 12.82 KB | ||
| 8 - From Testing to Production – Making AI Answers Safer with Evaluators | |||
| 1 -Testing the Untestable - Evaluating LLM Responses with Spring AI.mp4 | 104.46 MB | ||
| 1 -Testing the Untestable - Evaluating LLM Responses with Spring AI.vtt | 8.65 KB | ||
| 2 -Building Trustworthy AI with RelevancyEvaluator and FactCheckingEvaluator.mp4 | 95.77 MB | ||
| 2 -Building Trustworthy AI with RelevancyEvaluator and FactCheckingEvaluator.vtt | 13.72 KB | ||
| 3 -RelevancyEvaluator Demo - Ensuring LLMs Stay on Topic.mp4 | 130.54 MB | ||
| 3 -RelevancyEvaluator Demo - Ensuring LLMs Stay on Topic.vtt | 17.46 KB | ||
| 4 -FactCheckingEvaluator in Action - Catching AI Hallucinations.mp4 | 76.95 MB | ||
| 4 -FactCheckingEvaluator in Action - Catching AI Hallucinations.vtt | 10.2 KB | ||
| 5 -Using Spring AI Evaluator in a RAG Scenario.mp4 | 55.2 MB | ||
| 5 -Using Spring AI Evaluator in a RAG Scenario.vtt | 7.22 KB | ||
| 6 -Catching Bad LLM Answers in Production with Runtime Checks.mp4 | 71.07 MB | ||
| 6 -Catching Bad LLM Answers in Production with Runtime Checks.vtt | 10.6 KB | ||
| 7 -Runtime Validation with Spring AI Evaluator and Spring Retry.mp4 | 86.59 MB | ||
| 7 -Runtime Validation with Spring AI Evaluator and Spring Retry.vtt | 11.6 KB | ||
| 9 - Observability in Spring AI - Metrics, Monitoring & Tracing | |||
| 1 -Introduction to monitoring AI Operations.mp4 | 29.09 MB | ||
| 1 -Introduction to monitoring AI Operations.vtt | 5.24 KB | ||
| 2 -Enabling Metrics in Spring AI with Spring Boot Actuator - Part 1.mp4 | 84.62 MB | ||
| 2 -Enabling Metrics in Spring AI with Spring Boot Actuator - Part 1.vtt | 16.21 KB | ||
| 3 -Enabling Metrics in Spring AI with Spring Boot Actuator - Part 2.mp4 | 38.1 MB | ||
| 3 -Enabling Metrics in Spring AI with Spring Boot Actuator - Part 2.vtt | 6.76 KB | ||
| 4 -Enabling Prometheus Monitoring in Spring AI - Part 1.mp4 | 60.3 MB | ||
| 4 -Enabling Prometheus Monitoring in Spring AI - Part 1.vtt | 11.67 KB | ||
| 5 -Enabling Prometheus Monitoring in Spring AI - Part 2.mp4 | 34.99 MB | ||
| 5 -Enabling Prometheus Monitoring in Spring AI - Part 2.vtt | 7.03 KB | ||
| 6 -Setting Up Grafana Dashboard for Spring AI Metrics.mp4 | 69.99 MB | ||
| 6 -Setting Up Grafana Dashboard for Spring AI Metrics.vtt | 13.58 KB | ||
| 7 -Tracing AI Operations using OTLP & Jaeger - Part 1.mp4 | 61.58 MB | ||
| 7 -Tracing AI Operations using OTLP & Jaeger - Part 1.vtt | 9.19 KB | ||
| 8 -Tracing AI Operations using OTLP & Jaeger - Part 2.mp4 | 52.24 MB | ||
| 8 -Tracing AI Operations using OTLP & Jaeger - Part 2.vtt | 10.41 KB |
Description
Обучающие видео » Компьютерные видеоуроки и обучающие интерактивные DVD » Machine/Deep Learning, Neural Networks
From Java Dev to AI Engineer: Spring AI Fast Track
Год выпуска: 8/2025
Производитель: Udemy
Сайт производителя: https://www.udemy.com/course/java-spring-ai/
Автор: Madan Reddy, Eazy Bytes
Продолжительность: 13h 23m 2s
Тип раздаваемого материала: Видеоурок
Язык: Английский
Субтитры: Английский
Описание:
Build AI Apps with Spring AI, OpenAI, RAG, MCP, AI Testing, Observability, Speech & Image Generation
What you'll learn Build Spring Boot applications powered by Spring AI
Integrate Spring AI app with OpenAI, Ollama, Docker Model Runner, and AWS Bedrock
Use prompt templates and prompt stuffing techniques
Convert AI text responses to Java Beans, Lists, and Maps
Understand how LLMs work internally with tokens and embeddings
Implement Retrieval-Augmented Generation (RAG) with Spring AI
Implement memory in chat apps using Spring AI advisors
Teach LLMs to call tools exposed by Java methods Requirements Knowledge on Java, Spring Boot is mandatory Description
Are you ready to build AI-powered Java applications with real-world use cases? This hands-on course will teach you how to integrate cutting-edge AI capabilities into your Spring Boot applications using the Spring AI framework and OpenAI.
You’ll master everything from building your first chat-based app to using Retrieval-Augmented Generation (RAG), Tool Calling, Structured Output Conversion, MCP (Model Context Protocol), and even Speech-to-Text, Text-to-Speech, and Image Generation — all using Java and Spring Boot.
From understanding how LLMs work to deploying production-ready AI features with observability, testing, and advisor-based safety, this course is packed with powerful demos, clean explanations, and practical techniques to bring intelligence to your backend.
Whether you're a Java developer, Spring enthusiast, or backend engineer exploring Generative AI, this course will guide you step-by-step with best practices and battle-tested code.
[spoiler="What You’ll Learn:"]
Section 1: Welcome & Hello World with Spring AI Understand the Spring AI framework and course roadmap
Build your first Spring Boot AI app using OpenAI
Deep dive into ChatModel and ChatClient APIs Section 2: Prompt Engineering & Structured Output Use message roles, prompt templates, and stuffing techniques
Work with advisors to control AI behavior
Map AI responses to Java Beans, Lists, and Maps Section 3: Generative AI & LLM Fundamentals Learn about tokens, embeddings, and how LLMs generate text
Understand attention, vocabulary, and model internals
Explore static vs positional embeddings and context windows Section 4: AI Memory with ChatHistory Implement stateless-to-stateful conversations
Use MemoryAdvisors and Conversation IDs for per-user memory
Persist chat memory using JDBC and configure maxMessages Section 5: RAG – Retrieval-Augmented Generation Set up a vector store (Qdrant) using Docker
Store and query document embeddings in Spring Boot
Use RetrievalAugmentationAdvisor to feed documents to AI Section 6: Tool Calling – Let AI Take Action Enable tool invocation via LLMs
Build tools for real-time actions like querying time or database
Customize tool errors and return responses to users Section 7: Model Context Protocol (MCP) Learn MCP architecture and communication patterns
Build MCP Clients and Servers using Spring AI
Integrate with GitHub’s MCP Server and explore STDIO transport Section 8: Testing & Validating AI Outputs Use RelevancyEvaluator and FactCheckingEvaluator
Test AI responses for correctness in dev and production
Add runtime safety checks with Spring Retry Section 9: Observability – Monitoring AI Operations Enable Spring Boot Actuator metrics for AI
Set up Prometheus & Grafana dashboards
Trace AI behavior with OpenTelemetry and Jaeger Section 10: Speech & Image Generation Convert voice to text with AI-powered transcription
Generate natural speech from text prompts
Turn prompts into images using the ImageModel Who this course is for: Java and Spring Boot developers eager to integrate AI into real-world applications
Backend developers curious about LLMs, prompt engineering, and AI-powered workflows
Full Stack developers interested in adding AI capabilities to their microservices or APIs
Architects exploring Retrieval-Augmented Generation (RAG) and Tool Calling in Spring ecosystems
Professionals aiming to bring natural language interfaces to enterprise applications
Devs building chatbots, voice assistants, or image generation tools using Spring AI
Students and enthusiasts who want a practical, hands-on approach to Generative AI with Java
[/spoiler]
Формат видео: MP4
Видео: avc, 1280x720, 16:9, 30.000 к/с, 1075 кб/с
Аудио: aac lc, 48.0 кгц, 128 кб/с, 2 аудио
[spoiler="Изменения/Changes"]
The 2025/8 version has increased the number of lessons by 51 and the duration increased by 7 hours 4 minutes compared to 2025/7.
[/spoiler]
[spoiler="MediaInfo"]
General
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[/spoiler]
[spoiler="Скриншоты"]
[/spoiler]
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