Cloudera, the modern platform for machine learning and analytics optimized for the cloud, announced its strategic vision to accelerate the industrialization of enterprise machine learning (ML) and artificial intelligence (AI) – making the process of building, scaling, and deploying enterprise machine learning automated, repeatable and predictable.
Through Cloudera’s platform for ML, Cloudera Data Science Workbench development environment and applied AI advising and research offerings, Cloudera has enabled hundreds of enterprises to gain greater insight from their data, detect anomalous activity and predict events.
Now, Cloudera intends to expand its customers’ success in these areas with a vision for industrializing AI. Cloudera customers will be able to automate decisions at enterprise scale. Building, deploying and scaling ML/AI applications will become repeatable with what Cloudera calls an ‘AI Factory’ for turning data into decisions, at any scale, anywhere.
“The modern enterprise will be a network of intelligent applications powered by machine learning and AI. Right now the effort that goes into building ML and AI solutions is heavily skewed towards allocating and maintaining underlying infrastructure, instead of building a compounding set of capabilities that can deliver increased business values,” said Hilary Mason, general manager, Machine Learning at Cloudera. “AI deployments should be boring, meaning the process for deploying and scaling these types of applications should be routine. We should be paying attention to the value generated, not to the technology. This is why Cloudera is evolving its products and platform to industrialize the process of delivering AI solutions at enterprise scale.”
Today, no single platform unifies and powers all ML and AI workflows. Isolated ML and AI projects that employ disparate technology stacks result in duplicated efforts across the enterprise. Siloed infrastructure introduces quality issues and risk associated with security, governance, and compliance.
Reliance on bespoke solutions comes at the cost of internal skill building and differentiation. Lock-in to single vendor environments can limit the flexibility and agility needed to innovate and capitalize on new business opportunities. These common challenges present barriers to successfully operationalizing and scaling ML/AI capabilities at enterprise scale.
Industrialized enterprise ML and AI resolves these challenges, empowering businesses to build a repeatable AI Factory for turning data into decisions, at any scale, anywhere. Such an AI Factory requires a modern technical foundation; a platform for managing connected data workflows across multiple cloud and on-premise environments.
An AI Factory enables businesses to own and protect their data and intellectual property, maintaining control of their future and injecting AI into every part of the business that can be automated – reliably, predictably and securely.
Building on deep experience in machine learning, analytics and cloud, Cloudera plans to accelerate its roadmap to help customers more rapidly capitalize on the value of their data with ML and AI capabilities. Cloudera will deliver seamless, elastic, end-to-end machine learning workflows spanning public and private clouds as part of its recently announced enterprise data cloud strategy.
Cloudera will also expand support for the latest open data science tools, from Python and R to Spark and Tensorflow, to streamline unified data pipeline and AI model management at scale while minimizing cost and risk.