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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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Overview

Guides, examples and projects

NextStarter guide

Last updated 1 month ago

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Discover how to build production-ready ML pipelines with ZenML through our curated learning resources. Whether you're looking for step-by-step instructions, complete project implementations, or specific examples, you'll find resources to accelerate your ML workflow.

Guides

Step-by-step instructions to help you master ZenML concepts and features.

Projects

Complete end-to-end implementations that showcase ZenML in real-world scenarios.

Examples

Focused code snippets and templates that address specific ML workflow challenges.

See all examples in GitHub →
See all projects in our website →
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Research Radar

Automates research paper discovery and classification for specialized research domains.

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Starter Guide

Get started with ZenML fundamentals and set up your first pipeline

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Tutorials

Deep dives into advanced topics

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LLMOps Guide

Build and deploy Large Language Model pipelines

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ZenCoder

Your Own MLOps Engineer

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NightWatch

AI Database Summaries While You Sleep

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Magic Photobooth

A personalized AI image generation product that can create your avatars from a selfie.

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Sign Language Detection with YOLOv5

End-to-end computer vision pipeline

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ZenML Support Agent

A production-ready agent that can help you with your ZenML questions.

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GameSense

The LLM That Understands Gamers

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EuroRate Predictor

Turn European Central Bank data into actionable interest rate forecasts with this comprehensive MLOps solution.

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Quickstart

Bridging Local Development and Cloud Deployment

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End-to-End Batch Inference

Supervised ML project built with the ZenML framework and its integration.

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Basic NLP with BERT

Build NLP models with production-ready ML pipeline framework

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Computer Vision with YoloV8

End-to-end computer vision pipeline with modular design

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LLM Finetuning

LLM fine-tuning pipeline with PEFT approach