Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Oracle Data Miner empowers data scientists, "citizen data scientists," along with business and data analysts to interact seamlessly with data within the database through an intuitive graphical interface that utilizes a "drag and drop" workflow editor. As an extension of Oracle SQL Developer, Oracle Data Miner (ODMr) effectively captures and visually documents the analytical processes users follow while delving into data and crafting machine learning techniques. The workflows created with ODMr are instrumental for not only re-executing analytical methods but also for facilitating knowledge sharing among team members. Moreover, ODMr efficiently produces SQL and PL/SQL scripts while providing a workflow API that streamlines the deployment of models across the organization. By minimizing data movement, ensuring scalability for big data, maintaining security, and speeding up the transition from model development to deployment, organizations can effectively harness their data assets. This enhanced approach ultimately leads to more informed decision-making and improved business outcomes.
Description
Machine learning reveals concealed patterns and valuable insights within enterprise data, ultimately adding significant value to businesses. Oracle Machine Learning streamlines the process of creating and deploying machine learning models for data scientists by minimizing data movement, incorporating AutoML technology, and facilitating easier deployment. Productivity for data scientists and developers is enhanced while the learning curve is shortened through the use of user-friendly Apache Zeppelin notebook technology based on open source. These notebooks accommodate SQL, PL/SQL, Python, and markdown interpreters tailored for Oracle Autonomous Database, enabling users to utilize their preferred programming languages when building models. Additionally, a no-code interface that leverages AutoML on Autonomous Database enhances accessibility for both data scientists and non-expert users, allowing them to harness powerful in-database algorithms for tasks like classification and regression. Furthermore, data scientists benefit from seamless model deployment through the integrated Oracle Machine Learning AutoML User Interface, ensuring a smoother transition from model development to application. This comprehensive approach not only boosts efficiency but also democratizes machine learning capabilities across the organization.
API Access
Has API
No
API Access
Has API
Yes
Integrations
Apache Hive
No
Apache Spark
No
Impala
No
Kinetica
No
MySQL
No
Oracle Cloud Infrastructure
No
Oracle Database
No
PwC Check-In
No
Integrations
Apache Hive
Yes
Apache Spark
Yes
Impala
Yes
Kinetica
Yes
MySQL
Yes
Oracle Cloud Infrastructure
Yes
Oracle Database
Yes
PwC Check-In
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
Yes
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
Yes
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
Vendor Details
Company Name
Oracle
Founded
1977
Country
United States
Website
www.oracle.com/database/technologies/datawarehouse-bigdata/dataminer.html
Vendor Details
Company Name
Oracle
Founded
1977
Country
United States
Website
www.oracle.com/data-science/machine-learning/
Product Features
Data Mining
Data Extraction
No
Data Visualization
No
Fraud Detection
Yes
Linked Data Management
No
Machine Learning
No
Predictive Modeling
Yes
Semantic Search
No
Statistical Analysis
Yes
Text Mining
No
Product Features
Data Science
Access Control
No
Advanced Modeling
No
Audit Logs
No
Data Discovery
No
Data Ingestion
No
Data Preparation
No
Data Visualization
No
Model Deployment
No
Reports
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