Here’s a list of 100 chapter titles for a book on Prefect, with a focus on its use for artificial intelligence (AI). These chapters cover everything from basic concepts and workflow management to advanced AI tasks, data pipelines, and deployment strategies.
¶ Part 1: Introduction to Prefect and AI Basics
- What is Prefect? Introduction to Workflow Management for AI
- Setting Up Prefect for AI Projects
- Understanding Prefect's Core Concepts: Flows, Tasks, and Deployments
- Installing and Configuring Prefect for AI Workflows
- The Role of Prefect in Data Engineering and AI Pipelines
- Overview of Prefect’s UI: Monitoring and Managing AI Workflows
- Basic Prefect Workflows: Creating Your First AI Pipeline
- Managing Dependencies in Prefect for AI Projects
- Prefect Tasks and Parameters: Structuring AI Operations
- Running and Scheduling AI Tasks with Prefect
- Handling Failures and Retries in Prefect AI Pipelines
- Using Prefect for Parallel Execution of AI Workflows
- Understanding Prefect’s Dask Integration for Distributed AI Workflows
- Exploring Prefect’s Integration with Kubernetes for Scalable AI Pipelines
- Logging and Debugging AI Pipelines in Prefect
¶ Part 2: Data Management and Preprocessing in Prefect for AI
- Managing Data Pipelines with Prefect for AI Models
- Prefect for Efficient Data Collection and ETL (Extract, Transform, Load) in AI
- Data Cleaning and Transformation in Prefect Pipelines
- Preprocessing Text Data for NLP Applications in Prefect
- Handling Time-Series Data for AI Projects in Prefect
- Streaming Data Pipelines for Real-Time AI Analysis with Prefect
- Data Validation and Quality Checks with Prefect in AI Pipelines
- Using Prefect for Feature Engineering in AI
- Automating Data Preprocessing Pipelines in Prefect for Machine Learning
- Managing Large-Scale Datasets in Prefect Pipelines for AI
- Handling Missing Data and Imputation in Prefect for AI Models
- Data Splitting and Cross-Validation Pipelines for Machine Learning
- Integrating Prefect with Cloud Storage and Databases for AI Data Management
- Creating Custom Data Connectors for Prefect AI Pipelines
- Data Versioning and Reproducibility with Prefect for AI Projects
- Introduction to Machine Learning Pipelines with Prefect
- Creating Your First Machine Learning Model Pipeline in Prefect
- Integrating Prefect with Scikit-learn for Traditional Machine Learning Models
- Building Deep Learning Pipelines with TensorFlow and Prefect
- Using Prefect for Model Hyperparameter Optimization
- Cross-Validation and Grid Search for AI Model Tuning in Prefect
- Automating Model Training and Evaluation Pipelines with Prefect
- Handling Model Versioning and Experiment Tracking with Prefect
- Deploying ML Models using Prefect Pipelines
- Model Ensembling and Stacking in Prefect Pipelines for AI
- Scaling Machine Learning Pipelines with Prefect and Dask
- Using Prefect for Automated Model Retraining in AI Projects
- Monitoring and Reporting on AI Model Performance with Prefect
- Integration with Model Management Tools: Prefect and MLflow for AI
- Deploying Pretrained Models with Prefect Pipelines
- Introduction to NLP Workflows in Prefect
- Building Text Preprocessing Pipelines for NLP in Prefect
- Sentiment Analysis in Prefect: Automating Text Classification Pipelines
- Named Entity Recognition (NER) Pipelines with Prefect
- Building Word Embeddings Pipelines in Prefect for NLP
- Topic Modeling and Clustering with Prefect for NLP Tasks
- Building an Information Retrieval System with Prefect
- Text Summarization Pipelines in Prefect
- Machine Translation Pipelines with Prefect
- Using Prefect for Speech-to-Text NLP Pipelines
- Text Generation Models and Pipelines in Prefect
- Sequence-to-Sequence Modeling Pipelines with Prefect
- Integration of Deep Learning Models for NLP in Prefect Pipelines
- Using Transformers for NLP in Prefect Pipelines
- Automating Text Data Augmentation for NLP Projects with Prefect
- Introduction to Computer Vision Pipelines in Prefect
- Building Image Preprocessing Pipelines with Prefect for AI
- Object Detection Pipelines with Prefect
- Image Classification Pipelines with Prefect
- Segmentation Models and Pipelines in Prefect for Computer Vision
- Training Convolutional Neural Networks (CNNs) with Prefect
- Handling Image Augmentation in Prefect Pipelines
- Using Prefect for Real-Time Object Tracking Pipelines
- Generating Features for Computer Vision Models in Prefect
- Using Transfer Learning in Computer Vision Pipelines with Prefect
- Face Recognition Pipelines with Prefect
- Building Style Transfer Pipelines for AI Projects in Prefect
- Integrating Pretrained Models for Vision Tasks in Prefect
- Evaluating Image Models and Reporting Metrics in Prefect
- Deploying Computer Vision Models using Prefect Pipelines
- Introduction to Reinforcement Learning Pipelines in Prefect
- Building Q-Learning Pipelines with Prefect
- Deep Reinforcement Learning in Prefect: Implementing DQN
- Multi-Agent Systems and AI Collaboration with Prefect
- AutoML Pipelines with Prefect
- Implementing Neural Architecture Search (NAS) in Prefect
- Federated Learning Pipelines with Prefect
- Adversarial Machine Learning Pipelines in Prefect
- Neuroevolution in Prefect: Evolving AI Models with Prefect Pipelines
- AI for Graph Neural Networks (GNNs) in Prefect
- Explainability and Interpretability Pipelines for AI in Prefect
- Active Learning Pipelines with Prefect
- Transfer Learning Pipelines in Prefect for AI Applications
- Building an AI Research Pipeline for Reproducibility with Prefect
- Building a Hybrid Model Pipeline Combining ML and Deep Learning with Prefect
- Optimizing Prefect Pipelines for High-Performance AI Workflows
- Parallelism and Task Scheduling for AI Pipelines in Prefect
- Scaling AI Workflows with Prefect and Dask
- Efficient Data Storage and Retrieval in Prefect Pipelines for AI
- Prefect and Cloud: Running AI Pipelines on AWS, GCP, and Azure
- Using Prefect for GPU-Accelerated AI Model Training
- Optimizing Prefect Pipelines for Distributed Machine Learning
- Automating Model Selection and Hyperparameter Tuning in Prefect Pipelines
- Load Balancing and Fault Tolerance in Prefect AI Pipelines
- Cost Management and Optimization for AI Pipelines with Prefect
This list covers a comprehensive range of Prefect-specific topics tailored to AI, from basic pipeline creation to advanced AI concepts like reinforcement learning, model deployment, and optimization. It includes details on integrating Prefect with popular AI frameworks, working with different AI domains (NLP, computer vision), and ensuring scalability and reproducibility in AI workflows.