Cloudera is pitching itself as a “shovel to miners” essential for the Enterprise Artificial Intelligence (AI) Gold Rush, as it sees a future where customers no longer have to choose to control their on-premises infrastructure and the convenience of public clouds.
Speaking at the company's Evolve Asia-Pacific (APAC) event in Singapore this week, Cloudera CEO Charles Sansbury noted that more companies are opposed to the all-in-cloud migration, maintaining control over the most sensitive data, particularly for AI workloads.
“Two years ago, people told me how quickly you get all your workloads to the cloud. Now they're debating how many of their workloads will continue to exist,” Sunsbury said.
He argues that Cloudera is the only company capable of delivering analytics and AI hybrid data platforms across a variety of computing environments, making it important for modern data architectures. That was also why the company recently acquired Taikun, a platform for managing Kubernetes and cloud infrastructure across hybrid and multi-cloud environments.
Cloudera's Chief Product Officer Leo Brunnick and Chief Technology Officer Sergio Gago have parsed the acquisition of Taikun as a “missing piece” to realize the company's vision. They said it would provide customers with a single reference architecture with one codebase and user experience, eliminating the distinction between public and private cloud products. This provides a seamless experience of data engineering, AI inference, and data visualization, regardless of where the data or calculations reside.
The voice of key customers verifying Cloudera's data platform comes from Donald McDonald, managing director and head of the group data office at OCBC Bank. The bank's data team began customer analysis and marketing for AI use cases, but McDonald's wanted to take advantage of the great opportunities for transformation in the “materials” areas such as risk and compliance.
That was when the team decided to undertake a high stakes project in Financial Crime Compliance. “We used a traditional rules-based system for money laundering anti-money laundering,” McDonald said. “We were generating 12,000 alerts each month. All alerts took 40 minutes to investigate, and 98% of these alerts were falsely positive.
By applying AI to get alerts with an additional 500 features, OCBC was able to automatically handle low-risk alerts and hundreds of staff to higher value tasks. “Business has been able to know that data teams aren't merely working on marketing use cases,” McDonald said. “They are willing to take on material things that are at risk with many risks.”
McDonald saw the bank's ability to expand its AI efforts to three key pillars: people, platforms and data. The heart of the data and platform pillar is using Cloudera to build data lakes and enterprise data science platforms in a private cloud environment.
“Good AI doesn't happen without good data,” he said. “For the past decade, it has been in Cloudera where all data is centralized. Currently, over 350 systems are sitting in Cloudera, updated daily and 20 are updated in real time.”
OCBC's Unified Data Foundation combines the in-house Machine Learning Operations (MLOPS) framework with the AI Center of Excellence to drive AI projects across your organization and reuse data products. Also, large-scale use of bank automation saved data scientists an estimated 25-30% of their time, allowing them to do more with less.
Cloudera's Chief Revenue Officer Frank O'Dowd noted that the APAC region is a hotbed of innovation from customers like OCBC. “There's no market to show this better than APAC,” he said. “I share stories from what you do all over the world.”
