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MODEL REGISTRY
An open source platform for the machine learning lifecycle. Works with any ML library, language & existing code. Runs the same way in any cloud. Designed to scale from 1 user to large orgs. Scales to big data with Apache Spark™. MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility TUTORIALS AND EXAMPLES Tutorials and Examples. Below, you can find a number of tutorials and examples for various MLflow use cases. Train, Serve, and Score a Linear Regression Model. Hyperparameter Tuning. Orchestrating Multistep Workflows. Using the MLflow REST API Directly. Reproducibly run & share ML code. Packaging Training Code in a Docker Environment. MLFLOW — MLFLOW 1.17.0 DOCUMENTATION mlflow. log_metrics (metrics: Dict , step: Optional = None) → None Log multiple metrics for the current run. If no run is active, this method will create a new active run. Parameters. metrics – Dictionary of metric_name: String -> value: Float. Note that some special values such as +/- Infinity may be replaced by other values depending on the store.MLFLOW MODELS
An MLflow Model is a standard format for packaging machine learning models that can be used in a variety of downstream tools—for example, real-time serving through a REST API or batch inference on Apache Spark. The format defines a convention that lets you save a model in different “flavors” that can be understood by differentdownstream
MLFLOW PLUGINS
The entry point value (e.g. mlflow_test_plugin.sqlalchemy_store:PluginRegistrySqlAlchemyStore) specifies a custom subclass of mlflow.tracking.model_registry.AbstractStore (e.g., the PluginRegistrySqlAlchemyStore class within the mlflow_test_plugin module) The entry point name (e.g. file-plugin) is the tracking URI scheme with which to associate the custom AbstractStore MLFLOW MODEL REGISTRY MLflow Model Registry. The MLflow Model Registry component is a centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of an MLflow Model. It provides model lineage (which MLflow experiment and run produced the model), model versioning, stage transitions (for example from staging to production), and annotations. SEARCH — MLFLOW 1.17.0 DOCUMENTATION Python. Use the mlflow.tracking.MlflowClient.search_runs () or mlflow.search_runs () API to search programmatically. You can specify the list of columns to order by (for example, “metrics.rmse”) in the order_by column. The column can contain an optional DESC or ASC value; the default is ASC. The default ordering is to sort bystart_time
MLFLOW TRACKING
MLflow Tracking. The MLflow Tracking component is an API and UI for logging parameters, code versions, metrics, and output files when running your machine learning code and for later visualizing the results. MLflow Tracking lets you log and query experiments usingPython, REST, R
MLFLOW.XGBOOST
mlflow.xgboost. The mlflow.xgboost module provides an API for logging and loading XGBoost models. This module exports XGBoost models with the following flavors: XGBoost (native) format. This is the main flavor that can be loaded back into XGBoost.MLFLOW.KERAS
mlflow.keras. The mlflow.keras module provides an API for logging and loading Keras models. This module exports Keras models with the following flavors: Keras (native) format. This is the main flavor that can be loaded back into Keras. MLFLOW - A PLATFORM FOR THE MACHINE LEARNING LIFECYCLEDOCSCOMMUNITYMLFLOW 1.9 RELEASEDMLFLOW.LIGHTGBMMLFLOWMLFLOWMODEL REGISTRY
An open source platform for the machine learning lifecycle. Works with any ML library, language & existing code. Runs the same way in any cloud. Designed to scale from 1 user to large orgs. Scales to big data with Apache Spark™. MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility TUTORIALS AND EXAMPLES Tutorials and Examples. Below, you can find a number of tutorials and examples for various MLflow use cases. Train, Serve, and Score a Linear Regression Model. Hyperparameter Tuning. Orchestrating Multistep Workflows. Using the MLflow REST API Directly. Reproducibly run & share ML code. Packaging Training Code in a Docker Environment. MLFLOW — MLFLOW 1.17.0 DOCUMENTATION mlflow. log_metrics (metrics: Dict , step: Optional = None) → None Log multiple metrics for the current run. If no run is active, this method will create a new active run. Parameters. metrics – Dictionary of metric_name: String -> value: Float. Note that some special values such as +/- Infinity may be replaced by other values depending on the store.MLFLOW MODELS
An MLflow Model is a standard format for packaging machine learning models that can be used in a variety of downstream tools—for example, real-time serving through a REST API or batch inference on Apache Spark. The format defines a convention that lets you save a model in different “flavors” that can be understood by differentdownstream
MLFLOW PLUGINS
The entry point value (e.g. mlflow_test_plugin.sqlalchemy_store:PluginRegistrySqlAlchemyStore) specifies a custom subclass of mlflow.tracking.model_registry.AbstractStore (e.g., the PluginRegistrySqlAlchemyStore class within the mlflow_test_plugin module) The entry point name (e.g. file-plugin) is the tracking URI scheme with which to associate the custom AbstractStore MLFLOW MODEL REGISTRY MLflow Model Registry. The MLflow Model Registry component is a centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of an MLflow Model. It provides model lineage (which MLflow experiment and run produced the model), model versioning, stage transitions (for example from staging to production), and annotations. SEARCH — MLFLOW 1.17.0 DOCUMENTATION Python. Use the mlflow.tracking.MlflowClient.search_runs () or mlflow.search_runs () API to search programmatically. You can specify the list of columns to order by (for example, “metrics.rmse”) in the order_by column. The column can contain an optional DESC or ASC value; the default is ASC. The default ordering is to sort bystart_time
MLFLOW TRACKING
MLflow Tracking. The MLflow Tracking component is an API and UI for logging parameters, code versions, metrics, and output files when running your machine learning code and for later visualizing the results. MLflow Tracking lets you log and query experiments usingPython, REST, R
MLFLOW.XGBOOST
mlflow.xgboost. The mlflow.xgboost module provides an API for logging and loading XGBoost models. This module exports XGBoost models with the following flavors: XGBoost (native) format. This is the main flavor that can be loaded back into XGBoost.MLFLOW.KERAS
mlflow.keras. The mlflow.keras module provides an API for logging and loading Keras models. This module exports Keras models with the following flavors: Keras (native) format. This is the main flavor that can be loaded back into Keras. TUTORIALS AND EXAMPLES Tutorials and Examples. Below, you can find a number of tutorials and examples for various MLflow use cases. Train, Serve, and Score a Linear Regression Model. Hyperparameter Tuning. Orchestrating Multistep Workflows. Using the MLflow REST API Directly. Reproducibly run & share ML code. Packaging Training Code in a Docker Environment.MLFLOW MODELS
Storage Format. Each MLflow Model is a directory containing arbitrary files, together with an MLmodel file in the root of the directory that can define multiple flavors that the model can be viewed in.. Flavors are the key concept that makes MLflow Models powerful: they are a convention that deployment tools can use to understand the model, which makes it possible to write tools that work with TUTORIAL — MLFLOW 1.17.0 DOCUMENTATION Serving the Model. Now that you have packaged your model using the MLproject convention and have identified the best model, it is time to deploy the model using MLflow Models.An MLflow Model is a standard format for packaging machine learning models that can be used in a variety of downstream tools — for example, real-time serving through a REST API or batch inference on Apache Spark.MLFLOW TRACKING
MLflow Tracking. The MLflow Tracking component is an API and UI for logging parameters, code versions, metrics, and output files when running your machine learning code and for later visualizing the results. MLflow Tracking lets you log and query experiments usingPython, REST, R
MLFLOW MODEL REGISTRY MLflow Model Registry. The MLflow Model Registry component is a centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of an MLflow Model. It provides model lineage (which MLflow experiment and run produced the model), model versioning, stage transitions (for example from staging to production), and annotations. SEARCH — MLFLOW 1.17.0 DOCUMENTATION Search. The MLflow UI and API support searching runs within a single experiment or a group of experiments using a search filter API. This API is a simplified version of the SQL WHERE clause.MLFLOW.TYPES
mlflow.types. The mlflow.types module defines data types and utilities to be used by other mlflow components to describe interface independent of other frameworks or languages.. class mlflow.types. ColSpec (type: mlflow.types.schema.DataType, name: Optional = None) . Bases: object Specification of name and type of a single column in a dataset. property nameMLFLOW.SKLEARN
mlflow.sklearn. The mlflow.sklearn module provides an API for logging and loading scikit-learn models. This module exports scikit-learn models with the following flavors: This is the main flavor that can be loaded back into scikit-learn. Produced for use by generic PYTHON API — MLFLOW 1.17.0 DOCUMENTATION Python API. The MLflow Python API is organized into the following modules. The most common functions are exposed in the mlflow module, so we recommend starting there. mlflow. mlflow.azureml. mlflow.catboost. mlflow.deployments. mlflow.entities. mlflow.fastai. REST API — MLFLOW 1.17.0 DOCUMENTATION Create an experiment with a name. Returns the ID of the newly created experiment. Validates that another experiment with the same name does not already exist and fails if another experiment with the same namealready exists.
MLFLOW - A PLATFORM FOR THE MACHINE LEARNING LIFECYCLEDOCSCOMMUNITYMLFLOW 1.9 RELEASEDMLFLOW.LIGHTGBMMLFLOWMLFLOWMODEL REGISTRY
An open source platform for the machine learning lifecycle. Works with any ML library, language & existing code. Runs the same way in any cloud. Designed to scale from 1 user to large orgs. Scales to big data with Apache Spark™. MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility TUTORIALS AND EXAMPLES Tutorials and Examples. Below, you can find a number of tutorials and examples for various MLflow use cases. Train, Serve, and Score a Linear Regression Model. Hyperparameter Tuning. Orchestrating Multistep Workflows. Using the MLflow REST API Directly. Reproducibly run & share ML code. Packaging Training Code in a Docker Environment. MLFLOW MODEL REGISTRY MLflow Model Registry. The MLflow Model Registry component is a centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of an MLflow Model. It provides model lineage (which MLflow experiment and run produced the model), model versioning, stage transitions (for example from staging to production), and annotations.MLFLOW TRACKING
MLflow Tracking. The MLflow Tracking component is an API and UI for logging parameters, code versions, metrics, and output files when running your machine learning code and for later visualizing the results. MLflow Tracking lets you log and query experiments usingPython, REST, R
SEARCH — MLFLOW 1.17.0 DOCUMENTATION Python. Use the mlflow.tracking.MlflowClient.search_runs () or mlflow.search_runs () API to search programmatically. You can specify the list of columns to order by (for example, “metrics.rmse”) in the order_by column. The column can contain an optional DESC or ASC value; the default is ASC. The default ordering is to sort bystart_time
MLFLOW PLUGINS
The entry point value (e.g. mlflow_test_plugin.sqlalchemy_store:PluginRegistrySqlAlchemyStore) specifies a custom subclass of mlflow.tracking.model_registry.AbstractStore (e.g., the PluginRegistrySqlAlchemyStore class within the mlflow_test_plugin module) The entry point name (e.g. file-plugin) is the tracking URI scheme with which to associate the custom AbstractStore MLFLOW - A PLATFORM FOR THE MACHINE LEARNING LIFECYCLEDOCSCOMMUNITYMLFLOW 1.9 RELEASEDMLFLOW.LIGHTGBMMLFLOWMLFLOWMODEL REGISTRY
An open source platform for the machine learning lifecycle. Works with any ML library, language & existing code. Runs the same way in any cloud. Designed to scale from 1 user to large orgs. Scales to big data with Apache Spark™. MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility TUTORIALS AND EXAMPLES Tutorials and Examples. Below, you can find a number of tutorials and examples for various MLflow use cases. Train, Serve, and Score a Linear Regression Model. Hyperparameter Tuning. Orchestrating Multistep Workflows. Using the MLflow REST API Directly. Reproducibly run & share ML code. Packaging Training Code in a Docker Environment. MLFLOW MODEL REGISTRY MLflow Model Registry. The MLflow Model Registry component is a centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of an MLflow Model. It provides model lineage (which MLflow experiment and run produced the model), model versioning, stage transitions (for example from staging to production), and annotations.MLFLOW TRACKING
MLflow Tracking. The MLflow Tracking component is an API and UI for logging parameters, code versions, metrics, and output files when running your machine learning code and for later visualizing the results. MLflow Tracking lets you log and query experiments usingPython, REST, R
SEARCH — MLFLOW 1.17.0 DOCUMENTATION Python. Use the mlflow.tracking.MlflowClient.search_runs () or mlflow.search_runs () API to search programmatically. You can specify the list of columns to order by (for example, “metrics.rmse”) in the order_by column. The column can contain an optional DESC or ASC value; the default is ASC. The default ordering is to sort bystart_time
MLFLOW PLUGINS
The entry point value (e.g. mlflow_test_plugin.sqlalchemy_store:PluginRegistrySqlAlchemyStore) specifies a custom subclass of mlflow.tracking.model_registry.AbstractStore (e.g., the PluginRegistrySqlAlchemyStore class within the mlflow_test_plugin module) The entry point name (e.g. file-plugin) is the tracking URI scheme with which to associate the custom AbstractStoreMLFLOW.SKLEARN
mlflow.sklearn. The mlflow.sklearn module provides an API for logging and loading scikit-learn models. This module exports scikit-learn models with the following flavors: This is the main flavor that can be loaded back into scikit-learn. Produced for use by genericMLFLOW.DEPLOYMENTS
mlflow.deployments. Exposes experimental functionality for deploying MLflow models to custom serving tools. Note: model deployment to AWS Sagemaker and AzureML can currently be performed via the mlflow.sagemaker and mlflow.azureml modules, respectively. MLflow does not currently provide built-in support for any other deployment targets, but support for custom targets can be installed via thirdMLFLOW.KERAS
mlflow.keras. The mlflow.keras module provides an API for logging and loading Keras models. This module exports Keras models with the following flavors: Keras (native) format. This is the main flavor that can be loaded back into Keras.MLFLOW.XGBOOST
mlflow.xgboost. The mlflow.xgboost module provides an API for logging and loading XGBoost models. This module exports XGBoost models with the following flavors: XGBoost (native) format. This is the main flavor that can be loaded back into XGBoost. TUTORIAL — MLFLOW 1.17.0 DOCUMENTATION Serving the Model. Now that you have packaged your model using the MLproject convention and have identified the best model, it is time to deploy the model using MLflow Models.An MLflow Model is a standard format for packaging machine learning models that can be used in a variety of downstream tools — for example, real-time serving through a REST API or batch inference on Apache Spark.MLFLOW MODELS
An MLflow Model is a standard format for packaging machine learning models that can be used in a variety of downstream tools—for example, real-time serving through a REST API or batch inference on Apache Spark. The format defines a convention that lets you save a model in different “flavors” that can be understood by differentdownstream
MLFLOW — MLFLOW 1.17.0 DOCUMENTATION mlflow. log_metrics (metrics: Dict , step: Optional = None) → None Log multiple metrics for the current run. If no run is active, this method will create a new active run. Parameters. metrics – Dictionary of metric_name: String -> value: Float. Note that some special values such as +/- Infinity may be replaced by other values depending on the store.MLFLOW TRACKING
MLflow Tracking. The MLflow Tracking component is an API and UI for logging parameters, code versions, metrics, and output files when running your machine learning code and for later visualizing the results. MLflow Tracking lets you log and query experiments usingPython, REST, R
MLFLOW PLUGINS
The entry point value (e.g. mlflow_test_plugin.sqlalchemy_store:PluginRegistrySqlAlchemyStore) specifies a custom subclass of mlflow.tracking.model_registry.AbstractStore (e.g., the PluginRegistrySqlAlchemyStore class within the mlflow_test_plugin module) The entry point name (e.g. file-plugin) is the tracking URI scheme with which to associate the custom AbstractStoreMLFLOW.TYPES
mlflow.types. The mlflow.types module defines data types and utilities to be used by other mlflow components to describe interface independent of other frameworks or languages.. class mlflow.types. ColSpec (type: mlflow.types.schema.DataType, name: Optional = None) . Bases: object Specification of name and type of a single column in a dataset. property nameMLFLOW.TENSORFLOW
Refer to the autologging tracking documentation for more information on TensorFlow workflows. Parameters. every_n_iter – The frequency with which metrics should be logged. For example, a value of 100 will log metrics at step 0, 100, 200, etc. log_models – PYTHON API — MLFLOW 1.17.0 DOCUMENTATION Python API. The MLflow Python API is organized into the following modules. The most common functions are exposed in the mlflow module, so we recommend starting there. mlflow. mlflow.azureml. mlflow.catboost. mlflow.deployments. mlflow.entities. mlflow.fastai. MLFLOW.TRACKING.CLIENT class MlflowClient (object): """ Client of an MLflow Tracking Server that creates and manages experiments and runs, and of an MLflow Registry Server that creates and manages registered models and model versions. It's a thin wrapper around TrackingServiceClient and RegistryClient so there is a unified API but we can keep the implementation of the tracking and registry clients independent fromMLFLOW.XGBOOST
mlflow.xgboost. The mlflow.xgboost module provides an API for logging and loading XGBoost models. This module exports XGBoost models with the following flavors: XGBoost (native) format. This is the main flavor that can be loaded back into XGBoost. MLFLOW - A PLATFORM FOR THE MACHINE LEARNING LIFECYCLEDOCSCOMMUNITYMLFLOW 1.9 RELEASEDMLFLOW.LIGHTGBMMLFLOWMLFLOWMODEL REGISTRY
MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a centralmodel registry.
TUTORIALS AND EXAMPLES MLflow Project, a Series of LF Projects, LLC. All rights reserved. MLFLOW — MLFLOW 1.17.0 DOCUMENTATION mlflow. log_metrics (metrics: Dict , step: Optional = None) → None Log multiple metrics for the current run. If no run is active, this method will create a new active run. Parameters. metrics – Dictionary of metric_name: String -> value: Float. Note that some special values such as +/- Infinity may be replaced by other values depending on the store. TUTORIAL — MLFLOW 1.17.0 DOCUMENTATION Serving the Model. Now that you have packaged your model using the MLproject convention and have identified the best model, it is time to deploy the model using MLflow Models.An MLflow Model is a standard format for packaging machine learning models that can be used in a variety of downstream tools — for example, real-time serving through a REST API or batch inference on Apache Spark. MLFLOW MODEL REGISTRY Concepts. The Model Registry introduces a few concepts that describe and facilitate the full lifecycle of an MLflow Model. Model. An MLflow Model is created from an experiment or run that is logged with one of the model flavor’s mlflow..log_model() methods. Once logged, this model can then be registered with the Model Registry.MLFLOW TRACKING
MLflow Tracking. The MLflow Tracking component is an API and UI for logging parameters, code versions, metrics, and output files when running your machine SEARCH — MLFLOW 1.17.0 DOCUMENTATION Search. The MLflow UI and API support searching runs within a single experiment or a group of experiments using a search filter API. This API is a simplified version of the SQL WHERE clause.MLFLOW.SKLEARN
mlflow.sklearn. The mlflow.sklearn module provides an API for logging and loading scikit-learn models. This module exports scikit-learn models with the following flavors: Python (native) pickle format This is the main flavor that can be loaded back into scikit-learn.MLFLOW.SPARK
Enables (or disables) and configures logging of Spark datasource paths, versions (if applicable), and formats when they are read. This method is not threadsafe and assumes a SparkSession already exists with the mlflow-spark JAR attached. It should be called on the Spark driver, not on the executors (i.e. do not call this method within a function parallelized by Spark).MLFLOW.XGBOOST
mlflow.xgboost. The mlflow.xgboost module provides an API for logging and loading XGBoost models. This module exports XGBoost models with the following flavors: XGBoost (native) format. This is the main flavor that can be loaded back into XGBoost. MLFLOW - A PLATFORM FOR THE MACHINE LEARNING LIFECYCLEDOCSCOMMUNITYMLFLOW 1.9 RELEASEDMLFLOW.LIGHTGBMMLFLOWMLFLOWMODEL REGISTRY
MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a centralmodel registry.
TUTORIALS AND EXAMPLES MLflow Project, a Series of LF Projects, LLC. All rights reserved. MLFLOW — MLFLOW 1.17.0 DOCUMENTATION mlflow. log_metrics (metrics: Dict , step: Optional = None) → None Log multiple metrics for the current run. If no run is active, this method will create a new active run. Parameters. metrics – Dictionary of metric_name: String -> value: Float. Note that some special values such as +/- Infinity may be replaced by other values depending on the store. TUTORIAL — MLFLOW 1.17.0 DOCUMENTATION Serving the Model. Now that you have packaged your model using the MLproject convention and have identified the best model, it is time to deploy the model using MLflow Models.An MLflow Model is a standard format for packaging machine learning models that can be used in a variety of downstream tools — for example, real-time serving through a REST API or batch inference on Apache Spark. MLFLOW MODEL REGISTRY Concepts. The Model Registry introduces a few concepts that describe and facilitate the full lifecycle of an MLflow Model. Model. An MLflow Model is created from an experiment or run that is logged with one of the model flavor’s mlflow..log_model() methods. Once logged, this model can then be registered with the Model Registry.MLFLOW TRACKING
MLflow Tracking. The MLflow Tracking component is an API and UI for logging parameters, code versions, metrics, and output files when running your machine SEARCH — MLFLOW 1.17.0 DOCUMENTATION Search. The MLflow UI and API support searching runs within a single experiment or a group of experiments using a search filter API. This API is a simplified version of the SQL WHERE clause.MLFLOW.SKLEARN
mlflow.sklearn. The mlflow.sklearn module provides an API for logging and loading scikit-learn models. This module exports scikit-learn models with the following flavors: Python (native) pickle format This is the main flavor that can be loaded back into scikit-learn.MLFLOW.SPARK
Enables (or disables) and configures logging of Spark datasource paths, versions (if applicable), and formats when they are read. This method is not threadsafe and assumes a SparkSession already exists with the mlflow-spark JAR attached. It should be called on the Spark driver, not on the executors (i.e. do not call this method within a function parallelized by Spark).MLFLOW.XGBOOST
mlflow.xgboost. The mlflow.xgboost module provides an API for logging and loading XGBoost models. This module exports XGBoost models with the following flavors: XGBoost (native) format. This is the main flavor that can be loaded back into XGBoost. TUTORIAL — MLFLOW 1.17.0 DOCUMENTATION Serving the Model. Now that you have packaged your model using the MLproject convention and have identified the best model, it is time to deploy the model using MLflow Models.An MLflow Model is a standard format for packaging machine learning models that can be used in a variety of downstream tools — for example, real-time serving through a REST API or batch inference on Apache Spark.MLFLOW MODELS
Storage Format. Each MLflow Model is a directory containing arbitrary files, together with an MLmodel file in the root of the directory that can define multiple flavors that the model can be viewed in.. Flavors are the key concept that makes MLflow Models powerful: they are a convention that deployment tools can use to understand the model, which makes it possible to write tools that work with MLFLOW DOCUMENTATION MLflow Documentation. MLflow is an open source platform for managing the end-to-end machine learning lifecycle. It tackles four primaryfunctions:
MLFLOW PROJECTS
Project Directories. When running an MLflow Project directory or repository that does not contain an MLproject file, MLflow uses the following conventions to determine the project’s attributes:. The project’s name is the name of the directory. The Conda environment is specified in conda.yaml, if present.If no conda.yaml file is present, MLflow uses a Conda environment containing onlyMLFLOW TRACKING
MLflow Tracking. The MLflow Tracking component is an API and UI for logging parameters, code versions, metrics, and output files when running your machine QUICKSTART — MLFLOW 1.17.0 DOCUMENTATION Saving and Serving Models. MLflow includes a generic MLmodel format for saving models from a variety of tools in diverse flavors.For example, many models can be served as Python functions, so an MLmodel file can declare how each model should be interpreted as a Python function in order to let various tools serve it. MLflow also includes tools for running such models locally and exporting themMLFLOW PLUGINS
The entry point value (e.g. mlflow_test_plugin.sqlalchemy_store:PluginRegistrySqlAlchemyStore) specifies a custom subclass of mlflow.tracking.model_registry.AbstractStore (e.g., the PluginRegistrySqlAlchemyStore class within the mlflow_test_plugin module) The entry point name (e.g. file-plugin) is the tracking URI scheme with which to associate the custom AbstractStoreMLFLOW.MODELS
input_example – (Experimental) Input example provides one or several examples of valid model input. The example can be used as a hint of what data to feed the model. The given example will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format. COMMAND-LINE INTERFACE download. Download an artifact file or directory to a local directory. The output is the name of the file or directory on the local disk. Either --run-id or --artifact-uri must be provided.MLFLOW.XGBOOST
mlflow.xgboost. The mlflow.xgboost module provides an API for logging and loading XGBoost models. This module exports XGBoost models with the following flavors: XGBoost (native) format. This is the main flavor that can be loaded back into XGBoost. ☰ ✎ Edit navigationDocs
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* MLflow 1.8 Released! (21 Apr 2020) * MLflow 1.7.2 Released! (20 Mar 2020) * MLflow 1.7.1 Released! (17 Mar 2020) * MLflow is dropping Python 2 support in 1.8.0 (08 Mar 2020)* News Archive
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Works with any ML library, language & existing code*
Runs the same way in any cloud*
Designed to scale from 1 user to large orgs*
Scales to big data with Apache Spark™ MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. MLflow currently offers four components:*
MLFLOW TRACKING
Record and query experiments: code, data, config, and resultsRead more
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MLFLOW PROJECTS
Package data science code in a format to reproduce runs on anyplatform
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MLFLOW MODELS
Deploy machine learning models in diverse serving environmentsRead more
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MODEL REGISTRY
Store, annotate, discover, and manage models in a central repositoryRead more
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