Oracle has announced the availability of the Oracle Cloud Data Science Platform. At the core is Oracle Cloud Infrastructure Data Science, helping enterprises to collaboratively build, train, manage and deploy machine learning models to increase the success of data science projects. Oracle Cloud Infrastructure Data Science helps improve the effectiveness of data science teams with capabilities such as shared projects, model catalogs, team security policies, reproducibility and auditability, unlike other data science products that focus on individual data scientists. Through AutoML algorithm selection and tuning, model evaluation and model explanation, Oracle Cloud Infrastructure Data Science automatically selects the most optimal training datasets.
Today, organisations realise only a fraction of the enormous transformational potential of data because data science teams don't have easy access to the right data and tools to build and deploy effective machine learning models. With the net result stated that models take too long to develop, it doesn't always meet enterprise requirements for accuracy and robustness and therefore, never make it into production.
"Effective machine learning models are the foundation of successful data science projects, but the volume and variety of data facing enterprises can stall these initiatives before they ever get off the ground. With Oracle Cloud Infrastructure Data Science, we're improving the productivity of individual data scientists by automating their entire workflow and adding strong team support for collaboration to help ensure that data science projects deliver real value to businesses." Said Greg Pavlik, senior vice president product development, Oracle Data and AI Services.
Designed for Data Science Teams and Scientists
Oracle Cloud Infrastructure Data Science includes automated data science workflow, saving time and reducing errors with the following capabilities:
AutoML automated algorithm selection and tuning automates the process of running tests against multiple algorithms and hyperparameter configurations. It checks results for accuracy and confirms that the optimal model and configuration is selected for use. This saves significant time for data scientists and, more importantly, is designed to allow every data scientist to achieve the same results as the most experienced practitioners.
Automated predictive feature selection simplifies feature engineering by automatically identifying key predictive features from larger datasets.
Model evaluation generates a comprehensive suite of evaluation metrics and suitable visualizations to measure model performance against new data and can rank models over time to enable optimal behavior in production. Model evaluation goes beyond raw performance to take into account expected baseline behavior and uses a cost model so that the different impacts of false positives and false negatives can be fully incorporated.
Model explanation: Oracle Cloud Infrastructure Data Science provides automated explanation of the relative weighting and importance of the factors that go into generating a prediction. Oracle Cloud Infrastructure Data Science offers the first commercial implementation of model-agnostic explanation. With a fraud detection model, for example, a data scientist can explain which factors are the biggest drivers of fraud so the business can modify processes or implement safeguards.
Getting effective machine learning models successfully into production needs more than just dedicated individuals. It requires teams of data scientists working together collaboratively. Oracle Cloud Infrastructure Data Science delivers powerful team capabilities including:
Shared projects help users organise, enable version control and reliably share a team's work including data and notebook sessions.
Model catalogs enable team members to reliably share already-built models and the artifacts necessary to modify and deploy them.
Team-based security policies allow users to control access to models, code and data, which are fully integrated with Oracle Cloud Infrastructure Identity and Access Management.
Reproducibility and auditability functionalities enable the enterprise to keep track of all relevant assets, so that all models can be reproduced and audited, even if team members leave.
With Oracle Cloud Infrastructure Data Science, organisations can accelerate successful model deployment and produce enterprise-grade results and performance for predictive analytics to drive positive business outcomes.