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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 Kedro?
Kedro is an essential framework that promotes clean practices in the field of data science. By incorporating software engineering principles, it significantly boosts the productivity of machine-learning projects. A Kedro project offers a well-organized framework for handling complex data workflows and machine-learning pipelines. This structured approach enables practitioners to reduce the time spent on tedious implementation duties, allowing them to focus more on tackling innovative challenges. Furthermore, Kedro standardizes the development of data science code, which enhances collaboration and problem-solving among team members. The transition from development to production is seamless, as exploratory code can be transformed into reproducible, maintainable, and modular experiments with ease. In addition, Kedro provides a suite of lightweight data connectors that streamline the processes of saving and loading data across different file formats and storage solutions, thus making data management more adaptable and user-friendly. Ultimately, this framework not only empowers data scientists to work more efficiently but also instills greater confidence in the quality and reliability of their projects, ensuring they are well-prepared for future challenges in the data landscape.
Integrations Supported
Amazon SageMaker
Apache Airflow
Apache Spark
Azure Databricks
Azure Machine Learning
Dask
Docker
Gemini Enterprise Agent Platform
Jupyter Notebook
Kubeflow
API Availability
API Availability
Pricing Information
Pricing not provided
Pricing Information
Free
Free Version
Supported Platforms
SaaS
Supported Platforms
Linux
Customer Service / Support
Standard Support
Web-Based Support
Customer Service / Support
Web-Based Support
Training Options
Documentation Hub
Training Options
Documentation Hub
Company Facts
Organization Name
Oracle
Date Founded
1977
Company Location
United States
Company Website
www.oracle.com/data-science/
Company Facts
Organization Name
Kedro
Company Website
kedro.org
Categories and Features
Data Science
Not specified
Machine Learning
Not specified
Categories and Features
Data Science
Not specified