Mastering Spring Ai : Build Ai With Java

Published 9/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.88 GB | Duration: 8h 30m

Harness the Power of AI to Transform Your Java Applications

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

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