Learning Paths for Technical Professionals

LLMOps

This starter learning path provides a starter introduction to LLMOps, focusing on deploying, managing, and monitoring LLM-powered applications in production. Learners will explore pre-deployment strategies, advanced model management with MLflow, efficient deployment techniques, cost optimization, cluster management, real-time API development, and production monitoring using platforms like WhyLabs and Langkit.

Skills:

LLMOps deployment and management

Model evaluation and optimization

MLflow proficiency

Batching and quantization techniques

Cluster and distributed computing management

Real-time API development for LLMs

LLM production monitoring with WhyLabs and Langkit

Learning objectives:

  • Implement Pre-Deployment and Optimization Strategies: Apply evaluation, performance optimization, and best practices to ensure model correctness and efficiency before deployment.
  • Master Advanced Model Management: Utilize ML-Ops frameworks such as MLflow for model training, inference, and extending model functionality in production environments.
  • Deploy and Scale LLMs Efficiently: Employ batching, quantization, parallelism, and advanced scaling techniques like LoRa and ZeRO to optimize deployment and resource utilization.
  • Manage Large-Scale Deployments: Set up and manage distributed clusters using tools like RabbitMQ and Ray, and enable efficient data access and scaling for LLM applications.
  • Monitor and Maintain LLMs in Production: Leverage platforms like WhyLabs and Langkit to monitor, analyze, and ensure the reliability of LLM models in real-world scenarios.

Target audience:

This path is designed for machine learning engineers, data scientists, and DevOps professionals seeking to deploy, manage, and monitor large language models in production. It is also suitable for AI practitioners and technical leads aiming to optimize LLM operations and ensure robust, scalable AI solutions.

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