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What is Oracle Data Science?
A productivity-boosting data science platform presents exceptional features that streamline the crafting and evaluation of advanced machine learning (ML) models. By quickly utilizing data that businesses trust, organizations can enjoy enhanced flexibility and achieve their data-centric objectives through more straightforward ML model deployment. Cloud-based solutions empower companies to efficiently discover valuable insights that can shape their strategies. The process of building a machine learning model is inherently cyclical, and this ebook thoroughly explains each phase of its development. Users can interact with notebooks to create or assess a variety of machine learning algorithms, allowing for a hands-on learning experience. Engaging with AutoML not only leads to remarkable results in data science but also enables the swift generation of high-quality models with minimal effort. Additionally, automated machine learning techniques efficiently scrutinize datasets, suggesting the most effective features and algorithms while optimizing models and clarifying their outcomes. This holistic approach guarantees that organizations can fully exploit their data, fostering innovation and facilitating well-informed decision-making. Ultimately, adopting such advanced tools can significantly transform how businesses leverage data, setting them on a path toward lasting success.
What is Devron?
Utilizing machine learning on distributed datasets can lead to faster insights and better results, all while mitigating the costs, concentration risks, extended timelines, and privacy challenges that come with data centralization. The effectiveness of machine learning algorithms is frequently limited by the accessibility of diverse, high-quality data sources. By broadening access to a more extensive dataset and ensuring transparency in the outcomes of different models, organizations can gain deeper insights. The journey of obtaining necessary approvals, integrating data, and building the required infrastructure can be labor-intensive and lengthy. Nonetheless, by leveraging data in its original setting and adopting a federated and parallelized training strategy, organizations can rapidly develop trained models and extract valuable insights. In addition, Devron's ability to interact with data in its native context removes the need for data masking and anonymization, greatly reducing the challenges linked to data extraction, transformation, and loading. Consequently, this allows organizations to redirect their efforts towards analysis and strategic decision-making, rather than becoming bogged down by infrastructure issues. Ultimately, embracing these approaches can significantly enhance operational efficiency and innovation within organizations.
Integrations Supported
OCI Data Labeling
Oracle Cloud Infrastructure
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
Oracle
Date Founded
1977
Company Location
United States
Company Website
www.oracle.com/data-science/
Company Facts
Organization Name
Devron
Company Website
www.devron.ai/
Categories and Features
Data Science
Access Control
Advanced Modeling
Audit Logs
Data Discovery
Data Ingestion
Data Preparation
Data Visualization
Model Deployment
Reports
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization
Categories and Features
Data Management
Customer Data
Data Analysis
Data Capture
Data Integration
Data Migration
Data Quality Control
Data Security
Information Governance
Master Data Management
Match & Merge
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization