7952  Reviews star_rate star_rate star_rate star_rate star_half

Serving and Deploying Enterprise LLM Applications

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...

Read More
Course Code WA3514
Duration 4 days
Available Formats Classroom

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.

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

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

Course Details

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

Conclusion