Oracle Data Science Description
Data science platform that increases productivity and has unparalleled capabilities. Create and evaluate machine learning (ML), models of higher quality. Easy deployment of ML models can help increase business flexibility and enable enterprise-trusted data work faster. Cloud-based platforms can be used to uncover new business insights. Iterative processes are necessary to build a machine-learning model. This ebook will explain how machine learning models are constructed and break down the process. Use notebooks to build and test machine learning algorithms. AutoML will show you the results of data science. It is easier and faster to create high-quality models. Automated machine-learning capabilities quickly analyze the data and recommend the best data features and algorithms. Automated machine learning also tunes the model and explains its results.
Oracle Data Science Alternatives
Oracle Machine Learning
Machine learning uncovers hidden patterns in enterprise data and generates new value for businesses. Oracle Machine Learning makes it easier to create and deploy machine learning models for data scientists by using AutoML technology and reducing data movement. It also simplifies deployment. Apache Zeppelin notebook technology, which is open-source-based, can increase developer productivity and decrease their learning curve. Notebooks are compatible with SQL, PL/SQL and Python. Users can also use markdown interpreters for Oracle Autonomous Database to create models in their preferred language. No-code user interface that supports AutoML on Autonomous Database. This will increase data scientist productivity as well as non-expert users' access to powerful in-database algorithms to classify and regression. Data scientists can deploy integrated models using the Oracle Machine Learning AutoML User Interface.
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Neural Designer
Neural Designer is a data-science and machine learning platform that allows you to build, train, deploy, and maintain neural network models. This tool was created to allow innovative companies and research centres to focus on their applications, not on programming algorithms or programming techniques. Neural Designer does not require you to code or create block diagrams. Instead, the interface guides users through a series of clearly defined steps.
Machine Learning can be applied in different industries. These are some examples of machine learning solutions:
- In engineering: Performance optimization, quality improvement and fault detection
- In banking, insurance: churn prevention and customer targeting.
- In healthcare: medical diagnosis, prognosis and activity recognition, microarray analysis and drug design.
Neural Designer's strength is its ability to intuitively build predictive models and perform complex operations.
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cnvrg.io
An end-to-end solution gives you all the tools your data science team needs to scale your machine learning development, from research to production. cnvrg.io, the world's leading data science platform for MLOps (model management) is a leader in creating cutting-edge machine-learning development solutions that allow you to build high-impact models in half the time. In a collaborative and clear machine learning management environment, bridge science and engineering teams. Use interactive workspaces, dashboards and model repositories to communicate and reproduce results. You should be less concerned about technical complexity and more focused on creating high-impact ML models. The Cnvrg.io container based infrastructure simplifies engineering heavy tasks such as tracking, monitoring and configuration, compute resource management, server infrastructure, feature extraction, model deployment, and serving infrastructure.
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Dataiku DSS
Data analysts, engineers, scientists, and other scientists can be brought together. Automate self-service analytics and machine learning operations. Get results today, build for tomorrow. Dataiku DSS is a collaborative data science platform that allows data scientists, engineers, and data analysts to create, prototype, build, then deliver their data products more efficiently. Use notebooks (Python, R, Spark, Scala, Hive, etc.) You can also use a drag-and-drop visual interface or Python, R, Spark, Scala, Hive notebooks at every step of the predictive dataflow prototyping procedure - from wrangling to analysis and modeling. Visually profile the data at each stage of the analysis. Interactively explore your data and chart it using 25+ built in charts. Use 80+ built-in functions to prepare, enrich, blend, clean, and clean your data. Make use of Machine Learning technologies such as Scikit-Learn (MLlib), TensorFlow and Keras. In a visual UI. You can build and optimize models in Python or R, and integrate any external library of ML through code APIs.
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Integrations
Company Details
Company:
Oracle
Year Founded:
1977
Headquarters:
United States
Website:
www.oracle.com/data-science/
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Product Details
Platforms
SaaS
Type of Training
Documentation
Customer Support
Phone Support
Online
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