Serving and Deploying Enterprise LLM Applications


Course Number: AI-138WA
Duration: 4 days (26 hours)
Format: Live, hands-on

LLM Training Overview

This advanced Large Language Model (LLM) training is for Ops professionals who want to master deploying, managing, and scaling sophisticated LLM-based applications in enterprise environments. The course covers advanced topics such as scalable model serving infrastructures, monitoring and troubleshooting techniques, Agentic RAG deployment, and CI/CD and DevOps practices for LLM-based applications.

Location and Pricing

Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.

In addition, some courses are available as live, instructor-led training from one of our partners.

Objectives

  • Design and implement scalable model serving infrastructures for LLM-based applications, leveraging Kubernetes and serverless technologies for optimal performance and high availability
  • Optimize model serving performance and cost-efficiency by implementing advanced techniques like caching, compression, and quantization and leveraging spot instances and reserved capacity
  • Implement comprehensive monitoring and logging for LLM-based applications, setting up distributed tracing, metrics collection, and log aggregation. Utilize advanced dashboards, alerts, and automated troubleshooting for proactive issue resolution
  • Deploy and manage agentic RAG architectures at scale in production environments, ensuring scalability, fault tolerance, and optimized performance through monitoring and resource utilization
  • Streamline LLM-based application deployments with advanced CI/CD pipelines, integrating automated testing, staging, and production deployments while leveraging GitOps and infrastructure-as-code practices for efficient collaboration

Prerequisites

  • Practical programming skills in Python and familiarity with LLM concepts and frameworks (3+ Months LLM, 6+ Months Python and Machine Learning)
    • LLM Access via API, Open Source Libraries (HuggingFace)
    • LLM Application development experience (RAG, Chatbots, etc)
  • Strong understanding of containerization, orchestration, and cloud computing concepts
  • Experience with monitoring, logging, and troubleshooting of production systems
  • Familiarity with DevOps practices and CI/CD pipelines
    • MLOps knowledge preferred but not required

Outline

Expand All | Collapse All

Advanced Model Serving Infrastructure and Scalability
  • Designing and implementing scalable model serving infrastructures for LLM-based applications
    • Leveraging Kubernetes and serverless technologies for auto-scaling and high availability
    • Implementing multi-region and multi-cloud deployment strategies for scale
  • Optimizing model serving performance and cost-efficiency
    • Implementing advanced caching, compression, and quantization techniques for model serving
    • Leveraging spot instances, reserved capacity, and other cost optimization strategies
  • Implementing a scalable and cost-efficient model serving infrastructure for an LLM-based application
Monitoring, Logging, and Troubleshooting for LLM-Based Applications
  • Implementing advanced monitoring and logging techniques for LLM-based applications
    • Setting up distributed tracing, metrics collection, and log aggregation for LLM-based applications
    • Implementing advanced monitoring dashboards and alerts for key performance and quality metrics
  • Troubleshooting and root cause analysis for LLM-based application issues
    • Leveraging advanced debugging, profiling, and visualization tools for identifying performance bottlenecks and errors
    • Implementing automated anomaly detection and incident management workflows for LLM-based applications
  • Setting up comprehensive monitoring, logging, and troubleshooting for an LLM-based application
    • Configuring distributed tracing, metrics collection, and log aggregation
    • Implementing monitoring dashboards, alerts, and automated troubleshooting
Deploying and Managing Agentic RAG Architectures at Scale
  • Deploying and managing Agentic RAG architectures in production environments
    • Designing and implementing scalable and fault-tolerant Agentic RAG deployment architectures
    • Leveraging containerization, orchestration, and serverless technologies for Agentic RAG deployment
  • Monitoring and optimizing Agentic RAG performance and resource utilization
    • Implementing advanced monitoring and profiling techniques for Agentic RAG components
    • Optimizing Agentic RAG deployments for cost-efficiency and performance at scale
  • Deploying and managing an Agentic RAG architecture in a production environment
CI/CD and DevOps Practices for LLM-Based Application Deployments
  • Implementing advanced CI/CD pipelines and workflows for LLM-based application deployments
    • Designing and implementing end-to-end CI/CD pipelines with automated testing, staging, and production deployments
    • Leveraging GitOps and infrastructure-as-code practices for declarative and version-controlled deployments
  • Adopting DevOps best practices for collaborative and efficient LLM-based application development and deployment
    • Implementing agile development methodologies and continuous feedback loops for LLM-based applications
    • Establishing effective collaboration and communication channels between development, ops, and data science teams
  • Implementing a CI/CD pipeline and DevOps practices for an LLM-based application deployment
    • Designing and implementing an end-to-end CI/CD pipeline with automated testing and deployment stages
Conclusion

Training Materials

All Generative AI training students receive comprehensive courseware.

Software Requirements

All attendees must have a modern web browser and an Internet connection.



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