Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
With Amazon SageMaker Pipelines, you can effortlessly develop machine learning workflows using a user-friendly Python SDK, while also managing and visualizing your workflows in Amazon SageMaker Studio. By reusing and storing the steps you create within SageMaker Pipelines, you can enhance efficiency and accelerate scaling. Furthermore, built-in templates allow for rapid initiation, enabling you to build, test, register, and deploy models swiftly, thereby facilitating a CI/CD approach in your machine learning setup. Many users manage numerous workflows, often with various versions of the same model. The SageMaker Pipelines model registry provides a centralized repository to monitor these versions, simplifying the selection of the ideal model for deployment according to your organizational needs. Additionally, SageMaker Studio offers features to explore and discover models, and you can also access them via the SageMaker Python SDK, ensuring versatility in model management. This integration fosters a streamlined process for iterating on models and experimenting with new techniques, ultimately driving innovation in your machine learning projects.
Description
DataKitchen is a DataOps platform designed to help data engineering and analytics teams improve data quality, automate workflows, and monitor complex data environments. The software provides three primary products covering automated data testing, end-to-end process observability, and data pipeline orchestration. DataOps Data Quality TestGen profiles database tables and columns to automatically generate validation tests based on data characteristics, patterns, and quality requirements. Its testing capabilities identify anomalies, schema changes, missing values, duplicate records, and other data issues that could affect downstream analytics. DataOps Process Observability monitors data journeys across multiple pipelines, datasets, applications, and teams to identify failures and processing delays. Its monitoring tools consolidate operational events, execution statuses, metrics, and test results into dashboards with configurable alerts. DataOps Automation coordinates pipeline execution, manages isolated development environments, automates deployments, and embeds testing throughout data workflows. The platform integrates with existing technologies such as Snowflake, Databricks, BigQuery, Redshift, Airflow, dbt, and Azure Data Factory without requiring organizations to replace their data infrastructure. DataKitchen offers Apache 2.0 open-source editions of TestGen and Observability, along with enterprise capabilities for collaboration, security, deployment management, and technical support.
API Access
Has API
No
API Access
Has API
No
Integrations
Amazon SageMaker
Yes
Amazon Web Services (AWS)
Yes
Docker
No
Git
No
GitHub
No
Google Cloud BigQuery
No
Jenkins
No
Jira
No
Kubernetes
No
PostgreSQL
No
Integrations
Amazon SageMaker
No
Amazon Web Services (AWS)
No
Docker
Yes
Git
Yes
GitHub
Yes
Google Cloud BigQuery
Yes
Jenkins
Yes
Jira
Yes
Kubernetes
Yes
PostgreSQL
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
No
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Amazon
Founded
2006
Country
United States
Website
aws.amazon.com/sagemaker/pipelines/
Vendor Details
Company Name
DataKitchen
Founded
2013
Country
United States
Website
datakitchen.io
Product Features
Continuous Delivery
Application Lifecycle Management
No
Application Release Automation
No
Build Automation
No
Build Log
No
Change Management
No
Configuration Management
No
Continuous Deployment
No
Continuous Integration
No
Feature Toggles / Feature Flags
No
Quality Management
No
Testing Management
No
Continuous Integration
Build Log
No
Change Management
No
Configuration Management
No
Continuous Delivery
No
Continuous Deployment
No
Debugging
No
Permission Management
No
Quality Assurance Management
No
Testing Management
No
Machine Learning
Deep Learning
No
ML Algorithm Library
No
Model Training
No
Natural Language Processing (NLP)
No
Predictive Modeling
No
Statistical / Mathematical Tools
No
Templates
No
Visualization
No
Product Features
Automated Testing
Hierarchical View
No
Move & Copy
No
Parameterized Testing
No
Requirements-Based Testing
No
Security Testing
No
Supports Parallel Execution
No
Test Script Reviews
No
Unicode Compliance
No
Data Quality
Address Validation
No
Data Deduplication
No
Data Discovery
No
Data Profililng
No
Master Data Management
No
Match & Merge
No
Metadata Management
No