This course runs for a duration of 2 Days.
The class will run daily from 10 AM ET to 6 PM ET.
Class Location: Virtual LIVE Instructor Led - Virtual Live Classroom.
Deploy and scale generative AI in production across specialized hardware and complex data flows. GenAI deployment foundations cover the deployment lifecycle, architecture options including serverless and microservices, key challenges of latency, cost, and scalability, model-serving solutions including Hugging Face, TensorFlow Serving, and TorchServe, and MLOps for GenAI. Model packaging, containerization, and deployment strategies cover serializing and exporting models, Docker containerization, optimized image builds, dependency and environment management, cloud platforms including SageMaker, Vertex AI, and Azure ML, on-premise and hybrid deployments, edge deployment, Kubernetes orchestration, and serverless GenAI deployments. Monitoring, scaling, optimization, security, and reliability cover GenAI monitoring fundamentals, horizontal and vertical scaling, load balancing and auto-scaling, inference optimization through quantization, pruning, and distillation, caching, cost optimization, API endpoint security, input validation, error handling and failover, GDPR and CCPA compliance, and security audits. Hands-on labs produce a containerized GenAI model, a Kubernetes deployment, an auto-scaled application, and a basic security audit. The course is designed for DevOps engineers and software developers with practical deployment experience.
Skills Gained
By the end of this course, participants will be able to:
This course is designed for:
This course assists organizations to:
Introduction to Generative AI Deployment
By the end of this module, you will be able to recognize the deployment lifecycle for generative AI, choose between deployment architectures, identify the key challenges (latency, cost, scalability), compare model-serving solutions, and apply MLOps practices to GenAI.
Understanding the Deployment Lifecycle for Generative AI
Deployment Architectures for GenAI (Serverless, Microservices, etc.)
Key Challenges in Deploying Generative AI Models (Latency, Cost, Scalability)
Comparing and Contrasting Model Serving Solutions (Hugging Face, TensorFlow Serving, TorchServe)
Introduction to MLOps for Generative AI
Hands-on Lab: Evaluate deployment options for a sample GenAI application and stand up a basic MLOps pipeline for a text-generation model.
Model Packaging and Containerization
By the end of this module, you will be able to serialize and export GenAI models, containerize them with Docker, build optimized images for inference, manage dependencies and environments, and package models for specific frameworks and hardware.
Serializing and Exporting GenAI Models (ONNX, PMML)
Containerization with Docker
Building Optimized Docker Images for Generative AI
Managing Dependencies and Environments
Packaging Models for Specific Frameworks and Hardware
Hands-on Lab: Containerize a pre-trained GenAI model and optimize the Docker image for faster inference.
Deployment Strategies and Infrastructure
By the end of this module, you will be able to deploy generative AI to cloud platforms (SageMaker, Vertex AI, Azure ML), choose between on-premise and hybrid options, deploy to edge for low-latency, orchestrate with Kubernetes, and run serverless GenAI deployments.
Cloud-Based Deployment (AWS SageMaker, Google Vertex AI, Azure ML)
On-Premise and Hybrid Deployments
Edge Deployment for Low-Latency Applications
Kubernetes for Orchestrating GenAI Deployments
Serverless Deployments with AWS Lambda or Azure Functions
Hands-on Lab: Deploy a GenAI model to a cloud platform and to Kubernetes, comparing the operational tradeoffs.
Basics of Generative AI Monitoring
By the end of this module, you will be able to distinguish ongoing monitoring from offline evaluation, choose key monitoring metrics for an LLM application in production, instrument an alert and log pipeline, and verify the monitoring system end-to-end.
Differences Between Evaluation and Monitoring
Identifying Key Monitoring Metrics
Understanding the Monitoring Workflow
Alerts, Logs, and Monitoring Verification
Setting Up a Monitoring System
Hands-on Lab: Stand up a monitoring system for one GenAI application with key metrics, alert thresholds, and verification.
Scaling and Optimizing Generative AI Deployments
By the end of this module, you will be able to apply horizontal and vertical scaling to GenAI, configure load balancing and auto-scaling, optimize inference through quantization and pruning, apply caching for latency, and tune cost.
Horizontal and Vertical Scaling Strategies
Load Balancing and Auto-Scaling for GenAI Applications
Optimizing Model Inference for Performance (Quantization, Pruning, Distillation)
Caching Strategies for Improved Latency
Cost Optimization Techniques for GenAI Deployments
Hands-on Lab: Implement auto-scaling for a GenAI application and apply model-optimization techniques to reduce inference latency.
Security and Reliability in Generative AI Deployments
By the end of this module, you will be able to secure GenAI API endpoints, validate and sanitize inputs, implement robust error handling and failover, apply data-privacy compliance controls, and conduct security audits and penetration tests.
Securing API Endpoints and Access Control
Input Validation and Sanitization to Prevent Attacks
Implementing Robust Error Handling and Failover Mechanisms
Ensuring Data Privacy and Compliance (GDPR, CCPA)
Regular Security Audits and Penetration Testing
Hands-on Lab: Implement API authentication and authorization, then conduct a basic security audit of one GenAI deployment.
Participants should enter this course with:
Course Comments
All attendees must have a modern web browser and an Internet connection.