|
gptkbp:instance_of
|
gptkb:open-source_software
|
|
gptkbp:community_support
|
Active community
|
|
gptkbp:components
|
gptkb:Apache_Hive
Tracking
Models
Projects
|
|
gptkbp:deployment
|
gptkb:cloud_computing
Local
On-premises
|
|
gptkbp:developed_by
|
gptkb:Databricks
|
|
gptkbp:has_documentation
|
Available online
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
MLflow
|
|
gptkbp:integration
|
gptkb:Google_Cloud_AI_Platform
gptkb:Apache_Spark
gptkb:Hadoop
gptkb:AWS_Sage_Maker
gptkb:Azure_ML
|
|
gptkbp:language
|
gptkb:Java
gptkb:Python
gptkb:R
gptkb:Scala
|
|
gptkbp:latest_version
|
Model versioning
|
|
gptkbp:license
|
Apache License 2.0
|
|
gptkbp:model
|
REST API
Docker container
Java function
Python function
R function
MLflow Model format
|
|
gptkbp:model_serving
|
Batch serving
Real-time serving
|
|
gptkbp:primary_use
|
Managing the ML lifecycle
|
|
gptkbp:release_date
|
gptkb:2018
|
|
gptkbp:storage
|
gptkb:Data
gptkb:cloud_storage
SQL Database
|
|
gptkbp:supports
|
gptkb:XGBoost
gptkb:Tensor_Flow
gptkb:Keras
gptkb:Py_Torch
gptkb:Scikit-learn
multiple ML libraries
|
|
gptkbp:use_case
|
Collaboration among data scientists
Experiment tracking
Deployment of models
Model management
|
|
gptkbp:user_interface
|
gptkb:CLI
REST API
Web UI
|
|
gptkbp:bfsParent
|
gptkb:Databricks
gptkb:Py_Torch
|
|
gptkbp:bfsLayer
|
4
|