Certainly! Here’s a list of 100 chapter titles for a book on Kedro, focusing on artificial intelligence, progressing from beginner to advanced concepts:
¶ Part 1: Introduction to Kedro and AI Foundations
- Getting Started with Kedro for AI Development
- Installing Kedro and Setting Up Your Environment
- Introduction to Kedro: What It Is and Why It Matters for AI
- Understanding Kedro's Core Concepts and Architecture
- How Kedro Helps Structure and Manage AI Projects
- Overview of Data Pipelines and Their Role in AI
- Building Your First Kedro Pipeline
- Exploring Kedro's Modular Pipeline Structure
- Understanding the Kedro Project Directory Structure
- Managing Data and Parameters in Kedro Pipelines
- Setting Up Kedro with Machine Learning Projects
- Integrating Jupyter Notebooks with Kedro for AI
- Working with Kedro's Data Catalog for Data Management
- Understanding the Basics of Data Engineering for AI
- Best Practices for Project Organization in Kedro
- Building Your First Machine Learning Pipeline in Kedro
- Preprocessing Data in Kedro Pipelines
- Feature Engineering in Kedro for AI Models
- Data Splitting and Train-Test Validation with Kedro
- Training a Simple AI Model with Kedro Pipelines
- Automating Hyperparameter Tuning with Kedro
- Model Validation and Cross-Validation in Kedro Pipelines
- Saving and Loading Models in Kedro
- Deploying ML Models with Kedro Pipelines
- Building Reproducible ML Workflows in Kedro
- Integrating Model Evaluation Metrics in Kedro
- Versioning Data, Models, and Pipelines with Kedro
- Parallelizing Kedro Pipelines for Efficient AI Workflows
- Visualizing Data Pipelines and AI Models with Kedro
- Data Provenance and Lineage in Kedro Pipelines
- Advanced Data Preprocessing Techniques in Kedro
- Handling Imbalanced Datasets in Kedro Pipelines
- Working with Time Series Data in Kedro
- Natural Language Processing (NLP) Pipelines with Kedro
- Building Image Processing Pipelines in Kedro
- Creating a Recommendation System with Kedro
- Integrating AI Models into Web Services with Kedro
- Scaling Machine Learning Pipelines with Kedro
- Distributed Computing with Kedro and Dask
- Advanced Hyperparameter Optimization in Kedro
- Implementing Deep Learning Models in Kedro
- Automating Model Retraining in Kedro Pipelines
- Advanced Model Monitoring in Kedro
- Building AI-Powered Data Transformations in Kedro
- Using Kedro’s Hooks to Automate Pipeline Behaviors
¶ Part 4: Data Engineering and AI Integration with Kedro
- Data Ingestion and Cleaning in Kedro Pipelines
- Integrating External Data Sources into Kedro
- Working with Structured and Unstructured Data in Kedro
- Creating AI Features Using Kedro's Feature Store
- Connecting Kedro to SQL and NoSQL Databases
- Optimizing Data Loading and Caching in Kedro
- Using Kedro’s Kedro-Viz for Pipeline Visualization
- Building Custom Data Loaders and Transformers in Kedro
- Data Pipeline Best Practices for AI Projects
- Integrating Cloud Data Sources and Storage with Kedro
- Data Pipeline Monitoring and Alerts with Kedro
- Versioning and Tracking Data Changes with Kedro
- Creating Custom Kedro Nodes for Specialized AI Workflows
- Optimizing the Performance of Data Pipelines in Kedro
- Testing and Debugging Kedro Pipelines for AI
- Building Deep Neural Networks in Kedro
- Implementing Reinforcement Learning Models with Kedro
- Generative Adversarial Networks (GANs) in Kedro Pipelines
- Building Natural Language Processing (NLP) Models in Kedro
- Creating AI-Based Time Series Forecasting Pipelines
- Using Kedro for Transfer Learning with Pretrained Models
- Building a Custom Model Serving API with Kedro
- AutoML Pipelines for Hyperparameter Optimization in Kedro
- Ensemble Learning with Kedro Pipelines
- Implementing Federated Learning with Kedro Pipelines
- Creating Custom AI Metrics and Loss Functions in Kedro
- Implementing Model Explainability and Interpretability with Kedro
- Advanced Model Evaluation and Validation Techniques in Kedro
- Building AI Applications for Real-Time Data with Kedro
- Deploying AI Models on Edge Devices Using Kedro
¶ Part 6: Automation, CI/CD, and Deployment for AI Pipelines
- CI/CD for Kedro Pipelines in AI Projects
- Automating Kedro Pipelines with Jenkins and GitLab CI
- Setting Up Automated Tests for AI Pipelines in Kedro
- Version Control and Collaboration with Kedro
- Deploying Kedro Pipelines to Kubernetes for AI
- Integrating Kedro with Cloud Platforms for AI Pipelines
- Using Kedro for Automated Model Deployment and Rollbacks
- Building AI-Powered Data API Endpoints with Kedro
- Serverless Deployment of Kedro AI Pipelines
- Managing Data and Model Pipelines in Production with Kedro
- Monitoring and Logging AI Pipelines in Kedro
- Scaling Kedro Pipelines for High-Volume AI Applications
- Containerizing Kedro Pipelines for Deployment
- Deploying Scalable Machine Learning Models with Kedro
- Implementing Continuous Integration for Kedro’s AI Models
- AI-Powered Recommendation Systems with Kedro
- Building AI-Powered Fraud Detection Pipelines in Kedro
- Creating Intelligent Chatbots with Kedro Pipelines
- Automated Document Classification with Kedro
- AI for Predictive Maintenance Using Kedro Pipelines
- Creating Intelligent Search Engines with Kedro AI Models
- Applying AI to Healthcare Data Pipelines with Kedro
- AI-Driven Demand Forecasting with Kedro Pipelines
- Optimizing Marketing Campaigns Using AI in Kedro
- Future Trends and Innovations in AI with Kedro
These chapters offer a structured progression from understanding the basics of Kedro to applying it to advanced AI techniques, covering various types of AI models, integrations, deployment practices, and real-world applications.