Sure! Here are 100 chapter titles for a comprehensive guide to Amazon SageMaker Endpoints, covering topics from beginner to advanced in the context of artificial intelligence (AI):
¶ Introduction to Amazon SageMaker and Endpoints (Beginner)
- Introduction to Amazon SageMaker and Machine Learning
- Understanding the Basics of AI and ML
- Overview of SageMaker Endpoints in AI Workflows
- Setting Up Your First Amazon SageMaker Account
- Navigating the SageMaker Console for Model Deployment
- Key Concepts in Amazon SageMaker: Models, Endpoints, and Deployments
- Amazon SageMaker Pricing: Understanding Costs for Endpoints
- Understanding the Role of Endpoints in Real-Time AI Applications
- Choosing the Right Instance Types for SageMaker Endpoints
- Introduction to SageMaker Endpoints for Model Inference
¶ Creating and Deploying Models (Beginner)
- Introduction to Model Training in Amazon SageMaker
- How to Create a SageMaker Model from a Pre-trained Algorithm
- Building and Training Custom Models in SageMaker
- Using SageMaker's Built-in Algorithms for AI and ML
- Deploying Models as Endpoints in Amazon SageMaker
- Creating a Simple AI Model for Real-Time Inference
- Deploying Your First AI Model on SageMaker Endpoint
- Using the SageMaker SDK for Model Deployment
- SageMaker and Jupyter Notebooks: A Unified Environment for Deployment
- Understanding Model Deployment Lifecycle in SageMaker
- Scaling SageMaker Endpoints for High-Volume Traffic
- Monitoring SageMaker Endpoint Performance with CloudWatch
- Handling Inference Errors and Failures on SageMaker Endpoints
- Versioning and Updating Models on SageMaker Endpoints
- Multi-model Endpoints: Deploying Multiple Models to One Endpoint
- Customizing Inference Code with SageMaker Hosting Containers
- Best Practices for Managing Endpoint Deployment at Scale
- Securing SageMaker Endpoints with SSL and IAM Roles
- Load Testing Your SageMaker Endpoints for Performance
- Optimizing Endpoint Inference Latency and Cost
- Real-Time vs Batch Inference: Choosing the Right Approach
- Endpoint Auto-scaling with SageMaker: Scaling to Demand
- Using SageMaker Multi-Model Endpoints for Cost Efficiency
- Deploying AI Models with Custom Containers on SageMaker Endpoints
- Creating and Managing Endpoint Variants for A/B Testing
- Handling Multi-Model Endpoints and Traffic Routing
- Building a Robust API for Model Inference via SageMaker Endpoints
- Automating Endpoint Deployments with AWS Lambda Functions
- Managing Model Endpoints with SageMaker Pipelines
- Integrating SageMaker Endpoints with Other AWS Services for AI Workflows
- Model Explainability and Debugging Inference on Endpoints
- Deploying Deep Learning Models with SageMaker Endpoints
- Building and Deploying NLP Models using SageMaker Endpoints
- Integrating SageMaker Endpoints with Computer Vision Models
- Edge Deployment: Using SageMaker Endpoints with IoT Devices
- Real-Time Inference for Time Series Forecasting on SageMaker
- Deploying Reinforcement Learning Models with SageMaker
- Using SageMaker Endpoints for Anomaly Detection Models
- Deploying GANs (Generative Adversarial Networks) on SageMaker Endpoints
- Managing Advanced TensorFlow and PyTorch Deployments on SageMaker
¶ Security and Compliance with SageMaker Endpoints
- Configuring Endpoint Security for AI Models in SageMaker
- Using Amazon VPC with SageMaker for Secure Inference
- Setting Up Authentication and Authorization for SageMaker Endpoints
- Enabling Encryption for Data at Rest and in Transit on SageMaker
- Using AWS KMS to Encrypt Model Artifacts and Endpoint Data
- Auditing SageMaker Endpoints with AWS CloudTrail
- Integrating SageMaker Endpoints with AWS Secrets Manager
- Compliance Considerations for Deploying AI Models with SageMaker
- Ensuring Secure API Access to SageMaker Endpoints
- Role-Based Access Control (RBAC) for SageMaker Endpoints
¶ Monitoring and Troubleshooting SageMaker Endpoints
- Setting Up CloudWatch Metrics for SageMaker Endpoints
- Analyzing Inference Logs for SageMaker Endpoints
- Debugging Model Performance Issues on SageMaker Endpoints
- Using SageMaker Debugger to Monitor Model Inference
- Tracking Endpoint Request Latency and Throughput
- Monitoring AI Model Drift and Retraining Needs
- Using CloudWatch Alarms to Automate Endpoint Health Checks
- Troubleshooting Common Errors in SageMaker Endpoint Inference
- Analyzing Endpoint Errors Using SageMaker Logs and CloudWatch Insights
- Automating Recovery from Endpoint Failures with AWS Lambda
- Integrating SageMaker Endpoints with AWS Lambda for Serverless Inference
- Using AWS Step Functions to Orchestrate Endpoint Inference
- Real-Time AI Inference with SageMaker and Amazon Kinesis
- Building Scalable AI Pipelines Using SageMaker and AWS Glue
- Deploying AI Models with SageMaker and AWS AppSync for GraphQL APIs
- Integrating SageMaker Endpoints with Amazon API Gateway
- Combining SageMaker Endpoints with Amazon Redshift for Data Insights
- Creating End-to-End AI Solutions with SageMaker and AWS Data Pipeline
- Integrating SageMaker with Amazon S3 for Real-Time Data Fetching
- Using Amazon CloudFront for Low-Latency Access to SageMaker Endpoints
¶ AI and Model Optimization with SageMaker
- Optimizing Model Performance for Inference with SageMaker Endpoints
- Using SageMaker Neo for Optimizing Models for Edge Devices
- Model Quantization Techniques for Faster Inference on SageMaker
- Leveraging TensorFlow Serving for Deploying Custom AI Models
- Optimizing PyTorch Models for SageMaker Endpoints
- Accelerating Inference with GPU-Optimized SageMaker Endpoints
- Using SageMaker Automatic Model Tuning for Improved Performance
- Integrating SageMaker with AWS Inferentia for Cost-Effective Inference
- Optimizing SageMaker Endpoints for Cost Efficiency
- Using Model Pruning for Efficient Inference on SageMaker Endpoints
¶ Scaling and Cost Management for SageMaker Endpoints
- Cost Optimization Strategies for SageMaker Endpoints
- Managing High-Traffic Inference Requests with SageMaker
- Setting Up Auto-Scaling for SageMaker Endpoints Based on Traffic
- Fine-Tuning Instance Selection to Lower SageMaker Endpoint Costs
- Understanding Amazon SageMaker Endpoint Pricing Models
- Scaling AI Workloads with SageMaker Serverless Inference
- Choosing Between SageMaker Endpoint vs Batch Inference for Cost-Efficiency
- Estimating Costs for Real-Time vs Batch Inference with SageMaker
- Leveraging AWS Savings Plans for SageMaker Endpoint Usage
- Managing Endpoint Costs Through Resource Tagging in SageMaker
These chapter titles offer a thorough exploration of all aspects of using Amazon SageMaker Endpoints for AI applications, from foundational knowledge to advanced techniques for optimization, security, and cost management.