Amazon Sage Maker Studio Pipelines
GPTKB entity
Statements (70)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Sage
|
| gptkbp:allows |
data scientists to build, manage, and automate workflows
|
| gptkbp:automated |
model training and tuning
|
| gptkbp:can_be_used_for |
feature engineering
data drift detection |
| gptkbp:can_be_used_to |
create custom workflows
automate data ingestion manage model lifecycle schedule pipeline executions |
| gptkbp:can_be_used_with |
gptkb:Jupyter_notebooks
|
| gptkbp:enables |
team collaboration
data preprocessing data validation model monitoring collaboration among data scientists reproducible machine learning experiments |
| gptkbp:facilitates |
model evaluation
model deployment |
| https://www.w3.org/2000/01/rdf-schema#label |
Amazon Sage Maker Studio Pipelines
|
| gptkbp:includes |
step functions
|
| gptkbp:integrates_with |
gptkb:Amazon_Cloud_Watch
gptkb:Amazon_Redshift gptkb:Amazon_S3 gptkb:AWS_Lambda gptkb:Sage |
| gptkbp:is_accessible_by |
gptkb:AWS_Management_Console
|
| gptkbp:is_available_in |
multiple AWS regions
|
| gptkbp:is_compatible_with |
Docker containers
Python SDK |
| gptkbp:is_designed_for |
enterprise machine learning projects
|
| gptkbp:is_designed_to |
reduce manual effort in ML workflows
|
| gptkbp:is_optimized_for |
gptkb:performance
scalability |
| gptkbp:is_part_of |
AWS ecosystem
AWS AI services machine learning lifecycle |
| gptkbp:is_used_by |
data scientists
|
| gptkbp:is_used_for |
experiment tracking
model deployment automation |
| gptkbp:offers |
integration with third-party tools
real-time monitoring security features cost management features version control for models visualization of pipeline execution |
| gptkbp:provides |
monitoring and logging capabilities
user-friendly interface cloud-based infrastructure resource management tools automated machine learning workflows data source integration pipeline orchestration integration with Git Hub customizable pipeline templates end-to-end machine learning solutions |
| gptkbp:provides_access_to |
data preparation tools
|
| gptkbp:supports |
data transformation
batch processing data exploration data labeling hyperparameter optimization real-time inference model retraining multi-model endpoints multiple machine learning frameworks data science best practices A/ B testing CI/ CD for machine learning |
| gptkbp:bfsParent |
gptkb:Amazon_Web_Services_AI
|
| gptkbp:bfsLayer |
5
|