08 — Model Deployment Strategies
08 — Model Deployment Strategies
Section titled “08 — Model Deployment Strategies”Module Introduction
Section titled “Module Introduction”- Introduction
- Model Deployment Challenges
- AWS Solutions
Foundation Model Invocation Strategies
Section titled “Foundation Model Invocation Strategies”- Introduction
- Approach 1: On-Demand Invocation with AWS Lambda Functions
- Key Characteristics and Use Cases
- Implementation Architecture
- Approach 2: Provisioned Throughput with Amazon Bedrock
- Key Benefits and Use Cases
- Implementation Architecture
- Approach 3: Hybrid Solutions with Amazon SageMaker AI Endpoints
- Key Advantages and Capabilities
- Implementation Architecture
- Using Your Own Frontier Model Trained In Nova Forge
- Strategy Selection and Comparison
- AWS AI Factories
Containerization For Generative Ai Apps
Section titled “Containerization For Generative Ai Apps”- Introduction
- Containerization for Generative AI
- Advantages and Considerations of Containerization for Generative AI
- Containerized Generative AI Use Cases
- Containerized Generative AI Implementation
- Considerations for Large Language Models
- Integration with AWS Container Services
Multi-model Orchestration Benefits
Section titled “Multi-model Orchestration Benefits”- Introduction
- Introduction to Multi-Model Orchestrations
- Key Advantages of Multi-Model Orchestration
- Multi-Model Use Cases and Applications
- Model Selection and Aggregation Strategies
Model Selection Frameworks
Section titled “Model Selection Frameworks”- Introduction
- Core Capabilities of Model Selection Frameworks
- Framework Architecture and Components
- Example Workflow: Model Selection
- Selection Criteria and Optimization Strategies
- Advanced Selection Techniques
Multi-model Orchestration Techniques
Section titled “Multi-model Orchestration Techniques”- Introduction
- Model Chaining for Complex Tasks
- Model Chaining Types
- Model Chaining Implementation Example
- Model Collections with Custom Aggregation Logic
- Advanced Orchestration Patterns
Benefits Of Safeguarding Ai Workflows
Section titled “Benefits Of Safeguarding Ai Workflows”- Introduction
- Safeguarding Mechanisms
- Resource optimization benefits
- Failure mitigation and resilience
- Controlling model behavior
Safeguarding Methods
Section titled “Safeguarding Methods”- Introduction
- AWS Step Functions for Stopping Conditions
- AWS Lambda Functions for Timeout Mechanisms
- IAM Policies for Resource Boundaries
- Circuit Breakers for Failure Mitigation
Module Summary
Section titled “Module Summary”- Recap and next steps
- Resources