Mastering Spring Ai : Build Ai With Java




What you'll learn
Gain a deep understanding of integrating AI functionalities with Spring Boot applications.
Learn how to utilize Spring AI to build intelligent, responsive systems using AI and machine learning.
Develop practical skills in implementing and deploying AI models within Spring-based projects.
Explore advanced techniques for enhancing application performance and user experience through AI-powered features
Requirements
For the Mastering Spring AI course, the requirements or prerequisites are: Basic Understanding of Java: Familiarity with Java programming language and object-oriented principles. Experience with Spring Framework: Prior experience with Spring Boot and core Spring concepts. Knowledge of AI Concepts: Basic understanding of AI and machine learning concepts. Development Environment Setup: Ability to set up a Java development environment and tools for coding and testing.
Description
Mastering Spring AICourse DescriptionDive into the cutting-edge world of AI integration with Spring Boot in this comprehensive course, "Mastering Spring AI". Perfect for Java developers looking to enhance their applications with powerful AI capabilities, this course will guide you through the process of seamlessly incorporating artificial intelligence into your Spring projects.What You'll Learn:Spring AI Fundamentals: Understand the core concepts of Spring AI and its integration with Spring Boot.AI Model Integration: Learn to integrate and work with various AI models, including language models and image generation tools.Image Processing and Generation: Explore image-related AI tasks within your Spring applications.Vector Databases: Learn to use vector databases for efficient AI data storage and retrieval.AI-Powered Search: Implement advanced search functionalities using AI techniques.Prompt Engineering: Master the art of crafting effective prompts for optimal AI model responses.Prerequisites:Basic knowledge of Java and Spring BootFamiliarity with RESTful API developmentUnderstanding of basic AI concepts (helpful but not required)Who This Course is For:Java developers looking to integrate AI into their applicationsSpring Boot enthusiasts interested in expanding their skillsetSoftware engineers aiming to stay current with AI trends in application developmentEmbark on this exciting journey to master Spring AI and transform your Spring applications with the power of artificial intelligence!
Overview
Section 1: Introduction
Lecture 1 Introduction
Section 2: Basic Concept of AI
Lecture 2 Artificial Intelligence (AI) overview
Lecture 3 AI Model
Lecture 4 GPT (Generative Pre-trained Transformer)
Section 3: Working with Open AI API
Lecture 5 Working WIth Open AI
Lecture 6 Sign up for Open AI Account
Lecture 7 Add Billing Information
Lecture 8 Generate API Keys
Lecture 9 Spring boot hello world with Open AI
Lecture 10 Explanation of the flow
Lecture 11 Understanding ChatResponse object
Lecture 12 Create model , service and controllers
Lecture 13 Prompts and Prompt Template
Lecture 14 Prompt Template Example
Lecture 15 Resource Template Example
Section 4: Spring AI - Structured Output
Lecture 16 Generating Structured Output
Lecture 17 Structured Output example with List and Map
Lecture 18 Structured Output example with Object
Section 5: RAG (Retrieval-Augmented Generation)
Lecture 19 Vector Store in AI
Lecture 20 Vector store/vector database and Embeddings
Lecture 21 RAG Example
Lecture 22 PGVector example
Lecture 23 Croma db example for similarity search
Lecture 24 Redis Vector db with spring ai
Lecture 25 MongoDB Atlas Vector Search
Lecture 26 Cassandra Vector Search Example
Lecture 27 Neo4j Vector Search Example
Lecture 28 Oracle Vector Search Example
Lecture 29 Milvus Vector Search Example
Lecture 30 Typesense Vector Search Example
Lecture 31 Weaviate Vector Search Example
Lecture 32 Qdrant Vector Search Example
Lecture 33 Opensearch Vector Search Example
Section 6: Function calling in Spring AI
Lecture 34 Function calling in Spring AI
Lecture 35 Function Calling example using OpenAI
Section 7: Image generation and audio (text to speech and speech to text)
Lecture 36 Image Generation API using Spring AI
Lecture 37 Generating image using Spring AI
Lecture 38 Text to Speech example using Spring AI
Lecture 39 Text to Speech properties
Lecture 40 Text to Speech Steaming audio
Lecture 41 Speech to Text : Transcription API
Section 8: Running LLM locally
Lecture 42 Install Ollama for Windows
Lecture 43 Install Ollama for Unix (ubuntu)
Lecture 44 Ollama Spring Boot demo
Lecture 45 LM Studio for running local LLM
Lecture 46 LM Studio example with spring boot
Lecture 47 Other AI Providers
Section 9: Other LLM Provider example
Lecture 48 Azure open ai example
Lecture 49 Amazon Bedrock setup and example
Lecture 50 Mistral AI Example
Lecture 51 Google Vertex Gemini setup and example
Section 10: Observability in Spring AI
Lecture 52 Observability example in Spring AI
Lecture 53 Tracing using zipkin
Section 11: ETL Pipeline
Lecture 54 ETL Pipeline Overview
Lecture 55 Document class
Lecture 56 ETL Pipeline demo
Lecture 57 Metadata enrichment
Lecture 58 Metadata Enrichment example
Lecture 59 ETL PDF Reader example
Lecture 60 ETL JSON Reader example
Lecture 61 ETL Markdown Reader example
Section 12: Best practices to store secret or api keys
Lecture 62 Storing secret
Lecture 63 Storing key in Google Secret Manager
Lecture 64 Storying key in AWS Secret Manager
Lecture 65 Storing key in Azure Key Vault
Section 13: Thank You
Lecture 66 Thank you
Beginners to learn Spring AI



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