What’s the key to successful data science? While it’s true that so much of it stems from the various people in roles up and down the workflow—the IT staff that put together the infrastructure that collects the data; the data scientists who build the models; and the business users who use the models to derive insights—the truth is that data science involves massive amounts of data. On a micro scale, each person is an equal link in a very long chain that goes from data collection to final insights.
In the real world, the sheer volume of big data means that it’s nearly impossible to manually process everything in an efficient, time-sensitive way. Enter machine learning, the key to making this possible. Machine learning is not just a tool used by data scientists for modeling; in fact, it is much more critical for the entire data journey.
“Companies are looking at new ways to service their customers, do things more efficiently in an organization, or do things more innovatively,” says Aali Masood, senior director of Big Data Go to Market. Masood recently presented an Oracle webcast called Accelerate Data Science on Oracle Cloud, which dove deeper into this issue.
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Machine learning clearly affects all elements of big data workflow, but how? Let's take a closer look at some examples outside of data modeling.
Data Management: When data flows into a data lake from different sources, it can be highly disorganized, especially if little organizational logistics have been prepared ahead of time. Machine learning can help with this by identifying consistent patterns in the preparation process. For example, machine learning can optimize the use of a data catalog to automatically apply metadata to specific types or segments of data, making discovery easier for more efficient model building.
Analytics: The above example showcases just one way machine learning can make the process of data preparation and exploration easier. For business analysts, machine learning accelerates many elements, from identifying key variables among data, to generating predictive insights and forecasting, to even assisting with report and visualization outputs.
While it's clear that machine learning is necessary for data science, what's the best way to make sure that it is available and seamlessly integrated for teams and organizations?
Meet The Oracle Data Science Platform
In his webinar, Masood explains that the Oracle Data Science Platform has machine learning at its heart. With ML, all elements and layers of data science and big data build outward from that. Masood specifically focuses on five elements that, when put together, support the entire data science lifecycle.
Oracle Cloud Infrastructure Data Science: This enterprise-grade platform allows data scientists to work the way they want to while collaborating to share projects and reproduce models. ML powers automated workflows and streamlines the model building process.
Oracle Machine Learning: Oracle Machine Learning is a scalable and production-ready platform that provides in-database ML. With available support for SQL, R, and Python, Oracle Machine Learning expedites deployment and automates data preparation.
Graph Analytics: Powered by Oracle Big Data Spatial and Graph (an option that is included as part of Oracle Cloud), graph analytics deliver powerful new ways to examine data via relationships. Machine learning accelerates this by generating predictive results and automating relationship discovery.
Oracle Big Data Platform: Under Oracle Big Data Platform, the complete data workflow—from ingestion to insights—becomes interconnected in one seamless platform. Consolidated data means greater access and greater flexibility, especially when utilizing the same machine learning engine across components.
Oracle Analytics Cloud: Powered by ML, Oracle Analytics Cloud enables business analysts to build models, generate visualizations, and offer predictive insights for forecasting, all on a self-service basis.
The Full Data Science Lifecycle
It’s clear that data science is not just about what data scientists do with modeling. It requires a concerted effort from end to end, from the infrastructure required to ingest data to the visualizations that report findings and the work of data scientists in between. As Masood explains in the webinar, “Being successful with data science requires being successful across the entire lifecycle. It has to do with data scientists, business analysts, and engineers all coming together.” Machine learning, then, keeps this flow going and in some cases even removes steps.
Masood talks about this and much more in the entire 30-minute presentation, including:
- How to efficiently manage the ML model lifecycle
- How data science teams can effectively collaborate
- How to locate and work with the right data to build ML models
To watch the full webinar for Accelerate Data Science with Oracle Cloud, simply register to receive an on-demand view link.
To learn more about how to get the most out of your big data, check out Oracle Data Science Cloud—and don't forget to subscribe to the Oracle Big Data blog to get the latest posts sent to your inbox. Also, follow us on Twitter @OracleBigData.