toc.md

Table of contents

Getting Started

• ⭐ Introduction
• 🧙 Installation
• 🪄 Core concepts
• 🤔 Deploying ZenML
  • Deploy with ZenML CLI
  • Deploy with Docker
  • Deploy with Helm
  • Deploy using HuggingFace Spaces
  • Deploy with custom images
  • Manage deployed services
    • Upgrade the version of the ZenML server
    • Troubleshoot the deployed server
    • Troubleshoot stack components
    • Custom secret stores
    • Secret management
• ☁️ ZenML Pro
  • System Architectures
  • ZenML SaaS
  • User Management

User Guide

• 🐣 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
  • Finetuning LLMs with ZenML

How-To

• 😸 Set up a project repository
  • Connect your git repository
  • Project templates
  • Best practices
• ⛓️ Build a pipeline
  • Use pipeline/step parameters
  • Configuring a pipeline at runtime
  • Step output typing and annotation
  • Control caching behavior
  • Schedule a pipeline
  • Deleting a pipeline
  • Compose pipelines
  • Automatically retry steps
  • Run pipelines asynchronously
  • Control execution order of steps
  • Using a custom step invocation ID
  • Name your pipeline runs
  • Use failure/success hooks
  • Hyperparameter tuning
  • Version pipelines
  • Access secrets in a step
  • Fetching pipelines
  • Get past pipeline/step runs
• 🚨 Trigger a pipeline
  • Trigger a pipeline from Python Client
  • Trigger a pipeline from another pipeline
  • Trigger a pipeline from REST API
• 📃 Use configuration files
  • How to configure a pipeline with a YAML
  • What can be configured
  • Runtime settings for Docker, resources, and stack components
  • Configuration hierarchy
  • Find out which configuration was used for a run
  • Autogenerate a template yaml file
• 🐳 Customize Docker builds
  • Docker settings on a pipeline
  • Docker settings on a step
  • Specify pip dependencies and apt packages
  • Use your own Dockerfiles
  • Which files are built into the image
  • Reuse Docker builds to speed up Docker build times
  • Use code repositories to automate Docker build reuse
  • Build the pipeline without running
  • Define where an image is built
• ⚒️ Manage stacks & components
  • Deploy a cloud stack
  • Register a cloud stack
  • Deploy stack/components using mlstacks
  • Reference secrets in stack configuration
  • Implement a custom stack component
  • Implement a custom integration
• 🚜 Train with GPUs
• 🌲 Control logging
  • View logs on the dashboard
  • Enable or disable logs storage
  • Set logging verbosity
  • Disable rich traceback output
  • Disable colorful logging
• 🗄️ Handle Data/Artifacts
  • How ZenML stores data
  • Return multiple outputs from a step
  • Delete an artifact
  • Organize data with tags
  • Get arbitrary artifacts in a step
  • Handle custom data types
  • Load artifacts into memory
  • Skipping materialization
  • Passing artifacts between pipelines
• 📊 Visualizing artifacts
  • Default visualizations
  • Creating custom visualizations
  • Displaying visualizations in the dashboard
  • Disabling visualizations
• 🪆 Use the Model Control Plane
  • Registering a Model
  • Deleting a Model
  • Associate a pipeline with a Model
  • Connecting artifacts via a Model
  • Controlling Model versions
  • Load a Model in code
  • Promote a Model
  • Linking model binaries/data to a Model
  • Load artifacts from Model
• 📈 Track metrics and metadata
  • Attach metadata to a model
  • Attach metadata to an artifact
  • Attach metadata to steps
  • Group metadata
  • Special Metadata Types
  • Fetch metadata within steps
  • Fetch metadata during pipeline composition
• 👨‍🎤 Popular integrations
  • Run on AWS
  • Run on GCP
  • Kubeflow
  • Kubernetes
  • MLflow
  • Skypilot
• 🔌 Connect services (AWS, GCP, Azure, K8s etc)
  • Service Connectors guide
  • Security best practices
  • Docker Service Connector
  • Kubernetes Service Connector
  • AWS Service Connector
  • GCP Service Connector
  • Azure Service Connector
  • HyperAI Service Connector
• 🐍 Configure Python environments
  • Handling dependencies
  • Configure the server environment
• 🔌 Connect to a server
  • Connect in with your User (interactive)
  • Connect with a Service Account
• 🔐 Interact with secrets
• 🐞 Debug and solve issues

Stack Components

• 📜 Overview
• 🔋 Orchestrators
  • Local Orchestrator
  • Local Docker Orchestrator
  • Kubeflow Orchestrator
  • Kubernetes Orchestrator
  • Google Cloud VertexAI Orchestrator
  • AWS Sagemaker Orchestrator
  • Tekton Orchestrator
  • Airflow Orchestrator
  • Skypilot VM Orchestrator
  • HyperAI Orchestrator
  • Develop a custom orchestrator
• 🏪 Artifact Stores
  • Local Artifact Store
  • Amazon Simple Cloud Storage (S3)
  • Google Cloud Storage (GCS)
  • Azure Blob Storage
  • Develop a custom artifact store
• 🐳 Container Registries
  • Default Container Registry
  • DockerHub
  • Amazon Elastic Container Registry (ECR)
  • Google Cloud Container Registry
  • Azure Container Registry
  • GitHub Container Registry
  • Develop a custom container registry
• 🧪 Data Validators
  • Great Expectations
  • Deepchecks
  • Evidently
  • Whylogs
  • Develop a custom data validator
• 📈 Experiment Trackers
  • Comet
  • MLflow
  • Neptune
  • Weights & Biases
  • Develop a custom experiment tracker
• 🏃‍♀️ Model Deployers
  • MLflow
  • Seldon
  • BentoML
  • Hugging Face
  • Develop a Custom Model Deployer
• 👣 Step Operators
  • Amazon SageMaker
  • Google Cloud VertexAI
  • AzureML
  • Spark
  • Develop a Custom Step Operator
• ❗ Alerters
  • Discord Alerter
  • Slack Alerter
  • Develop a Custom Alerter
• 🖼️ Image Builders
  • Local Image Builder
  • Kaniko Image Builder
  • Google Cloud Image Builder
  • Develop a Custom Image Builder
• 🏷️ Annotators
  • Argilla
  • Label Studio
  • Pigeon
  • Prodigy
  • Develop a Custom Annotator
• 📓 Model Registries
  • MLflow Model Registry
  • Develop a Custom Model Registry
• 📊 Feature Stores
  • Feast
  • Develop a Custom Feature Store

Examples

• 🚀 Quickstart
• 🔏 End-to-End Batch Inference
• 📚 Basic NLP with BERT
• 👁️ Computer Vision with YoloV8
• 📖 LLM Finetuning
• 🧩 More Projects...

Reference

• 🐍 Python Client
• 📼 Global settings
• 🌎 Environment Variables
• 👀 API reference
• 🤷 SDK & CLI reference
• 📚 How do I...?
• ♻️ Migration guide
  • Migration guide 0.13.2 → 0.20.0
  • Migration guide 0.23.0 → 0.30.0
  • Migration guide 0.39.1 → 0.41.0
  • Migration guide 0.58.2 → 0.60.0
• 💜 Community & content
• ❓ FAQ