Serving and Deploying Enterprise LLM Applications Training

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.

Course Details


4 days


  • 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

Skills Gained

  • Design and implement scalable and cost-efficient model serving infrastructures for LLM-based applications
  • Implement advanced monitoring, logging, and troubleshooting techniques for LLM-based applications in production
  • Deploy and manage Agentic RAG architectures at scale using containerization and orchestration technologies
  • Implement CI/CD pipelines and adopt DevOps best practices for efficient and collaborative LLM-based application deployment
Course Outline
  • 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