New Datameer SmartAI democratizes data science within organizations by operationalizing deep learning models with enterprise scale and governance
Furthering its mission to democratize data access within the enterprise, Datameer today unveiled SmartAI, an industry-first solution for operationalizing deep learning models directly within enterprise data lake environments securely and at scale. SmartAI finally allows enterprises to democratize data science, taking the deep learning work of data scientists from the lab to the business in production-ready scenarios that meet the big data security, governance and management standards IT requires. Now, business analysts can apply and execute trusted deep learning models against massive datasets from enterprise data lakes to drive better business outcomes.
"With all the promise of data science and artificial intelligence, organizations have had difficulty delivering on its business value. This is because it is difficult to operationalize the models at scale," said John L. Myers, Managing Research Director at Enterprise Management Associates (EMA), a Boulder, CO-based industry analysis firm. "Datameer's SmartAI functionality enables organizations to connect the machine and deep learning models created by data scientists to information-rich data lakes to fulfill this promise."
Taking Deep Learning From the Lab to the Data Lake
SmartAI combines the rich data management and pipelining capabilities of Datameer with Google TensorFlow, the fastest growing platform for an end-to-end deep learning analytic cycle. The combined Datameer/TensorFlow solution delivers the fastest time to deeper, business-ready insights through:
- Agile creation of rich analytic pipelines that feed deep learning models through Datameer's advanced integration, preparation and feature engineering features,
- Creation of deep learning models using TensorFlow's advanced algorithms and performance architecture,
- Analyst access to trusted deep learning models within the Datameer function library for easy one-click application of the model during analysis
- Direct operationalization of TensorFlow models into Datameer data pipelines to integrate models with scale, performance and governance
This seamless connection between the model and the data lake allows for scalable runtime execution of TensorFlow models with Datameer jobs, and easy integration of deep learning results to downstream applications and tools.
The addition of SmartAI into Datameer's industry-leading Hadoop-native data preparation and analytics platform means all of Datameer's robust security, governance, monitoring and other mission-critical operational requirements apply, satisfying stringent IT requirements and finally allowing deep learning to be applied to enterprise data lake environments. Whether deployed on-premises or in the cloud, private data is kept well secured and governed in Datameer as always, while it is leveraged for smarter, deep learning-driven predictive analytics.
"Today, we're only seeing the tip of the iceberg in terms of what can be accomplished in the world of deep learning and artificial intelligence," said Peter Voss, CTO of Datameer. "AI is only as good as the data that feeds it. We're thrilled to connect the dots by allowing enterprises to bring together massive amounts of disparate data, prepare and design the data pipeline, and now ultimately feed the data into models that have the potential to radically optimize business models."
Datameer SmartAI will initially be available as a technical preview.
Datameer is a big data analytics platform that helps companies create and extract value from enterprise data lakes. Leading brands such as Aetna, American Airlines, British Telecom, National Instruments, Priceline, Sophos and Telefonica all use Datameer to answer deeper business questions to improve market effectiveness, increase efficiency and/or reduce risk.
Using Datameer, organizations deliver these insights in hours instead of months and operationalize them immediately, increasing their business agility and responsiveness.