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  • Documentation
  • Learn
  • ZenML Pro
  • Stacks
  • API Reference
  • SDK Reference
  • Overview
  • Starter guide
    • Create an ML pipeline
    • Cache previous executions
    • Manage artifacts
    • Track ML models
    • A starter project
  • Production guide
    • Deploying ZenML
    • Understanding stacks
    • Connecting remote storage
    • Orchestrate on the cloud
    • Configure your pipeline to add compute
    • Configure a code repository
    • Set up CI/CD
    • An end-to-end project
  • LLMOps guide
    • RAG with ZenML
      • RAG in 85 lines of code
      • Understanding Retrieval-Augmented Generation (RAG)
      • Data ingestion and preprocessing
      • Embeddings generation
      • Storing embeddings in a vector database
      • Basic RAG inference pipeline
    • Evaluation and metrics
      • Evaluation in 65 lines of code
      • Retrieval evaluation
      • Generation evaluation
      • Evaluation in practice
    • Reranking for better retrieval
      • Understanding reranking
      • Implementing reranking in ZenML
      • Evaluating reranking performance
    • Improve retrieval by finetuning embeddings
      • Synthetic data generation
      • Finetuning embeddings with Sentence Transformers
      • Evaluating finetuned embeddings
    • Finetuning LLMs with ZenML
      • Finetuning in 100 lines of code
      • Why and when to finetune LLMs
      • Starter choices with finetuning
      • Finetuning with 🤗 Accelerate
      • Evaluation for finetuning
      • Deploying finetuned models
      • Next steps
  • Tutorials
    • Managing scheduled pipelines
    • Trigger pipelines from external systems
    • Hyper-parameter tuning
    • Inspecting past pipeline runs
    • Train with GPUs
    • Running notebooks remotely
    • Managing machine learning datasets
    • Handling big data
  • Best practices
    • 5-minute Quick Wins
    • Keep Your Dashboard Clean
    • Configure Python environments
    • Shared Components for Teams
    • Organizing Stacks Pipelines Models
    • Access Management
    • Setting up a Project Repository
    • Infrastructure as Code with Terraform
    • Creating Templates for ML Platform
    • Using VS Code extension
    • Leveraging MCP
    • Debugging and Solving Issues
    • Choosing an Orchestrator
  • Examples
    • Quickstart
    • End-to-End Batch Inference
    • Basic NLP with BERT
    • Computer Vision with YoloV8
    • LLM Finetuning
    • More Projects...
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