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 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
Outline
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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.
