When it comes to getting work done in the enterprise, it all depends on who does the heavy lifting.

Writing an email is a simple process today, but what if everyone who needed to write one had to first build the computer on which it’s written and create the network over which is would be sent? Chances are pretty good that not a lot of emails would get written.

A similar dilemma has confronted data scientists tasked with accessing information they need in order to answer questions and provide direct value for a business. When faced with the convoluted work necessary to build an infrastructure that would store and analyze a massive amount of information, data scientists got rapidly bogged down.

Enter BlueData Software Inc., a company designed to help enterprises accelerate artificial-intelligence and machine-learning deployments to speed delivery of data-driven results. Its mission is to provide a platform that lets data scientists focus on data while leaving the heavy lifting to others.

“It all started six years ago; it was a bold vision and a big idea,” said Kumar Sreekanti (pictured), co-founder and chief executive officer of BlueData. “No one was paying attention to how to make the data consumable in the enterprise. Our vision was to build a software infrastructure platform like VMware, especially focused on data-intensive distributed applications.”

Sreekanti spoke with Peter Burris (@plburris), host of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during a “Deploying AI in the Enterprise” digital community event at theCUBE’s studio in Palo Alto, California. Also speaking to Burris during the event were Ramesh Thyagarajan, executive director of The Advisory Board Co.; Anant Chintamaneni, vice president of products at BlueData; Nanda Vijaydev, chief data scientist of BlueData; and Ingrid Burton, chief marketing officer of H2O.ai Inc. They discussed BlueData’s container-based technology solutions, how the company has helped numerous industries, such as healthcare, the benefits offered to data scientists in their work, and the impact of a recent partnership with H2O.ai. (* Disclosure below.)

Market opportunity for HPE

The journey for BlueData has followed a path from startup to acquisition. In November, Hewlett Packard Enterprise Co. announced that it would purchase the firm under financial terms that were not disclosed.

The company began with a focus originally on analytics software with its flagship EPIC platform designed to make it easier for companies to leverage big data technologies. As BlueData progressed, it implemented new tools, such as AI/ML Accelerator, to facilitate easier enterprise entry into machine-learning deployments.

The market opportunity, which both BlueData and HPE recognized, was shaped by the rise of disruptive new companies, such as Uber and Airbnb, that rapidly built substantial businesses based on transforming data into value.

“They’ve been able to leverage open-source technologies, data science techniques, big data, fast data, all types of data to extract business value,” Chintamaneni said. “Enterprises of all sizes want to take advantage of those same assets.”

Containerized solution

Docker containers are the core ingredient behind BlueData’s software approach for the deployment of large-scale machine-learning environments. The company’s technology enables users to spin up containerized platforms in a matter of minutes, which allows data scientists to leverage infrastructure and tools, such as Spark, Kafka, TensorFlow and Hadoop, necessary to do their work.

The platform is designed to help accelerate enterprise deployments on-premises, in the public cloud, or within a hybrid architecture.

“We are the first end-to-end containerized enterprise solution that gives you distributed applications,” Sreekanti said. “We also built multitenancy so enterprises can run multiple workloads on the same data.”

Helping the patient journey

For BlueData’s customers, that access and agility can have an impact in meaningful ways. At the Advisory Board Co., a healthcare business that serves 90% of U.S. hospitals, the information technology group was seeking a solution to improve data access for the firm’s engineers, analysts and scientists.

The company’s problem centered on how to allocate infrastructure resources based on peak periods when hospitals would load massive amounts of data into the cloud. BlueData’s EPIC software, in combination with a Mesos scheduler for Apache Spark, allowed the company to power Spark containers as needed and then quickly scale back to free up resources.

“Being in healthcare, we need to be able to look at a large amount of data over a period of time in order to figure out how a patient’s health journey is happening,” Thyagarajan explained. “It is not just you having the data. You need to have a curated data asset process on top of a platform.”

BlueData’s solution has also had an impact on data scientists and how they perform tasks in the enterprise. In her role as BlueData’s chief data scientist, Vijaydev spends a great deal of time working with data science teams in varied industries across the company’s customer base. She often comes across scenarios where her counterparts require different compute capabilities depending on the job level.

Using BlueData EPIC, data scientists can move from a laptop-based analysis to a scalable and distributed model using a Hadoop or Spark cluster.

“With the growing types of computational algorithms that are available, there’s a lot of opportunity and, at the same time, there’s a lot of uncertainty,” Vijaydev said. “Having those controls taken care of well before I get to the picture as a data scientist makes it extremely easy for us to focus on the problem, focus on accessing the best of breed technology.”

Free of infrastructure worries

To provide an end-to-end solution for machine learning in the enterprise, BlueData has recently partnered with H2O.ai, an open-source AI software tool provider. The partnership allows joint customers to implement dev/test environments and production deployments for large-scale, distributed machine learning pipelines.

Developers can rapidly spin up containerized environments pre-provisioned with H2O’s libraries. The idea is to free data scientists from having to worry whether the infrastructure will provide them with the tools they need.

“Data scientists want to get to answers,” H2O’s Burton said. “They don’t want to do all of that heavy lifting; they want to solve a problem. We do automatic machine-learning platforms, optimizing algorithms and doing a lot of the heavy lifting that novice data scientists need and help expert data scientists as well.”

Behind BlueData’s partnership with H2O is an important message. The evolution of data science in the enterprise will take a village, the collective work of multiple partners to provide an infrastructure solution that will ultimately improve time-to-value at a reasonable cost.

“Data is a team sport,” Burton said. “It takes data scientists, businesses leaders and IT to make it work all together.”

Watch the entire “Deploying AI in the Enterprise” digital community event video below. (* Disclosure: Hewlett Packard Enterprise Co. sponsored the above segments of theCUBE. Neither HPE nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE