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MLRun basics
Using MLRun
MLRun architecture
MLRun ecosystem
MLOps development workflow
Tutorials and Examples
Quick start tutorial
Train, compare, and register models
Serving pre-trained ML/DL models
Projects and automated ML pipeline
Model monitoring and drift detection
Add MLOps to existing code
Batch inference and drift detection
Feature store example (stocks)
MLflow tracker
Demos
MLRun cheat sheet
Installation and setup guide
Install MLRun locally using Docker
Install MLRun on Kubernetes
Install MLRun on AWS
Set up your environment
Core components
Projects and automation
Create, save, and use projects
Git best practices
Load projects
Run, build, and deploy functions
Build and run workflows/pipelines
Working with secrets
MLRun project bootstrapping with
project_setup.py
Functions
Kinds of functions (runtimes)
Function of type
job
Function of type
serving
Dask distributed runtime
Running Dask on the cluster with MLRun
Pipelines using Dask, Kubeflow and MLRun
Databricks runtime
MPIJob and Horovod runtime
Spark Operator runtime
Nuclio real-time functions
Create and use functions
Converting notebooks to function
Attach storage to functions
Images and their usage in MLRun
Build function image
Node affinity
Function hub
Using a Git repo as a function hub
Data and artifacts
Data stores
Data items
Artifacts
Model Artifacts
Logging Artifacts
Feature store
Feature store overview
Feature sets
Sources and targets
Feature set transformations
Feature vectors
Feature store end-to-end demo
Part 1: Data ingestion
Part 2: Training
Part 3: Serving
Part 4: Automated ML pipeline
Batch runs and workflows
MLRun execution context
Decorators and auto-logging
Running a task (job)
Running a multi-stage workflow
Running multiple functions with ExitHandler
Running multiple functions in parallel
Running a conditional workflow
Configuring runs and functions
Scheduled jobs and workflows
Notifications
Real-time serving pipelines (graphs)
Getting started
Use cases
Graph concepts and state machine
Model serving graph
Writing custom steps
Built-in steps
Demos and tutorials
Distributed (multi-function) pipeline example
Advanced model serving graph - notebook example
Serving graph high availability configuration
Error handling
Model monitoring
MLOps tasks
Ingest and process data
Using data sources and items
Logging datasets
Ingest data using the feature store
Ingest features with Spark
Develop and train models
Model training and tracking
Create a basic training job
Working with data and model artifacts
Automated experiment tracking
Using the built-in training function
Hyperparameter tuning optimization
Training with the feature store
Deploy models and applications
Real-time serving
Using built-in model serving classes
Build your own model serving class
Test and deploy a model server
Model serving API
Serving with the feature store
Batch inference
Canary and rolling upgrades
Monitor and alert
Model monitoring overview
Enable model monitoring
CI/CD automation with Git
Create a project using a Git source
Load project YAML from Git, Zip, Tar source
Run pipelines with Github Actions, GitLab
References
Index
API by module
mlrun.frameworks
AutoMLRun
TensorFlow.Keras
PyTorch
SciKit-Learn
XGBoost
LightGBM
mlrun.common
mlrun.common.schemas
mlrun.common.schemas.artifact
mlrun.common.schemas.notification
mlrun
mlrun.artifacts
mlrun.config
mlrun.datastore
mlrun.db
mlrun.execution
mlrun.feature_store
mlrun.model
mlrun.model_monitoring
mlrun.package
mlrun.package.packager.Packager
mlrun.package.packagers.default_packager.DefaultPackager
mlrun.package.packagers_manager.PackagersManager
mlrun.package.errors
mlrun.package.packagers.python_standard_library_packagers
mlrun.package.packagers.python_standard_library_packagers.BoolPackager
mlrun.package.packagers.python_standard_library_packagers.BytearrayPackager
mlrun.package.packagers.python_standard_library_packagers.BytesPackager
mlrun.package.packagers.python_standard_library_packagers.DictPackager
mlrun.package.packagers.python_standard_library_packagers.FloatPackager
mlrun.package.packagers.python_standard_library_packagers.FrozensetPackager
mlrun.package.packagers.python_standard_library_packagers.IntPackager
mlrun.package.packagers.python_standard_library_packagers.ListPackager
mlrun.package.packagers.python_standard_library_packagers.NonePackager
mlrun.package.packagers.python_standard_library_packagers.PathPackager
mlrun.package.packagers.python_standard_library_packagers.SetPackager
mlrun.package.packagers.python_standard_library_packagers.StrPackager
mlrun.package.packagers.python_standard_library_packagers.TuplePackager
mlrun.package.packagers.numpy_packagers
mlrun.package.packagers.numpy_packagers.NumPyNDArrayDictPackager
mlrun.package.packagers.numpy_packagers.NumPyNDArrayListPackager
mlrun.package.packagers.numpy_packagers.NumPyNDArrayPackager
mlrun.package.packagers.numpy_packagers.NumPyNumberPackager
mlrun.package.packagers.numpy_packagers.NumPySupportedFormat
mlrun.package.packagers.pandas_packagers
mlrun.package.packagers.pandas_packagers.PandasDataFramePackager
mlrun.package.packagers.pandas_packagers.PandasSeriesPackager
mlrun.package.packagers.pandas_packagers.PandasSupportedFormat
mlrun.platforms
mlrun.projects
mlrun.run
mlrun.runtimes
mlrun.serving
storey.transformations - Graph transformations
Command-Line Interface
Glossary
Change log
Change log
Repository
Suggest edit
Open issue
.rst
.pdf
mlrun.common
mlrun.common
#
mlrun.common.schemas