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Python for AI
1. Python Fundamentals for AI
2. Essential Libraries for Data Science and AI
3. Machine Learning with Scikit-Learn
4. Deep Learning Frameworks
5. Specialized AI Applications
6. Model Deployment and Production
Model Deployment and Production
Model Serialization and Persistence
Serialization Formats
Pickle
Pickle Protocol
Security Considerations
Pickle Limitations
Joblib
Efficient Serialization
Compression Options
Parallel Processing Support
Framework-specific Formats
TensorFlow SavedModel
Model Structure
Signature Definition
Asset Management
Keras H5 Format
Model Architecture
Weights Storage
Custom Objects
PyTorch State Dictionary
Model Parameters
Optimizer State
Custom Serialization
Model Versioning
Version Control Strategies
Model Registry
Metadata Management
API Development for ML Models
REST API Fundamentals
HTTP Methods
Request and Response Formats
Status Codes
API Design Principles
Flask for ML APIs
Flask Application Structure
Route Definition
Request Handling
Response Generation
Error Handling
Model Loading and Inference
Input Validation
JSON Serialization
FastAPI for ML APIs
FastAPI Features
Automatic Documentation
Type Hints
Pydantic Models
Asynchronous Endpoints
Dependency Injection
Background Tasks
File Upload Handling
API Testing
Unit Testing APIs
Integration Testing
Load Testing
API Documentation
Containerization with Docker
Docker Fundamentals
Container Concepts
Docker Architecture
Images and Containers
Docker Registry
Dockerfile Creation
Base Image Selection
Dependency Installation
Code Copying
Environment Variables
Port Exposure
Entry Point Definition
Docker Operations
Building Images
Running Containers
Container Management
Volume Mounting
Network Configuration
Docker Best Practices
Multi-stage Builds
Layer Optimization
Security Considerations
Image Size Optimization
Docker Compose
Service Definition
Multi-container Applications
Environment Configuration
Service Dependencies
Cloud Deployment
Cloud Platform Overview
Infrastructure as a Service
Platform as a Service
Software as a Service
Serverless Computing
Amazon Web Services
EC2 for Model Hosting
S3 for Data Storage
SageMaker
Training Jobs
Model Endpoints
Batch Transform
Lambda for Serverless Inference
API Gateway
Google Cloud Platform
Compute Engine
Cloud Storage
AI Platform
Training Service
Prediction Service
Notebooks
Cloud Functions
Cloud Run
Microsoft Azure
Virtual Machines
Blob Storage
Azure Machine Learning
Compute Instances
Model Deployment
Automated ML
Azure Functions
Container Instances
Cloud-native ML Services
Managed Notebook Services
AutoML Services
Pre-trained APIs
Model Monitoring Services
MLOps and Production Considerations
MLOps Principles
Continuous Integration
Continuous Deployment
Model Monitoring
Data Versioning
Experiment Tracking
Model Monitoring
Performance Monitoring
Data Drift Detection
Model Drift Detection
Alert Systems
Scalability Considerations
Horizontal Scaling
Vertical Scaling
Load Balancing
Caching Strategies
Security in ML Systems
Data Privacy
Model Security
Access Control
Encryption
Production Best Practices
Error Handling
Logging
Health Checks
Graceful Degradation
A/B Testing for Models
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1. Python Fundamentals for AI