PALO ALTO, Calif., May 6, 2020 – Cloudera (NYSE: CLDR), the enterprise data cloud company, today announced an expanded set of production machine learning capabilities for MLOps is now available in Cloudera Machine Learning (CML). Organizations can manage and secure the ML lifecycle for production machine learning with CML's new MLOps features and Cloudera SDX for models. Data scientists, machine learning engineers, and operators can collaborate in a single unified solution, drastically reducing time to value and minimizing business risk for production machine learning models. "Companies past the piloting phase of machine learning adoption are looking to scale deployments in production to hundreds or even thousands of ML models across their entire business," said Andrew Brust, Founder and CEO of Blue Badge Insights. "Managing, monitoring and governing models at this scale can't be a bespoke process. With a true ML operations platform, companies can make AI a mission-critical component of their digitally transformed business."

Cloudera Machine Learning is a cloud service for creating self-service machine learning workspaces and the underlying compute clusters for teams of data scientists. Enterprise data science teams need access to business data and the tools and computing resources required for end-to-end machine learning workflows, while IT and the business need to maintain data governance and control infrastructure costs. Cloudera Machine Learning brings the agility and economics of cloud to self-service machine learning workflows with governed business data and tools that data science teams need, anywhere.

Today's modern enterprises are collecting data at exponential rates, and it's no mystery that effectively making use of that data has become a top priority for many. According to a recent survey of 2000 global enterprises by McKinsey & Company, 47% of organizations have embedded at least one AI capability in their standard business processes. This is up from 20% in 2017 and it's clear that this growth has created a global race to enabling the next important evolution of business as we know it: The AI-first enterprise. But what does this actually mean? With investment in AI technologies poised to reach $9.5 billion over the next three years, the imminent opportunity involves embedding data and machine learning intelligence across the business at scale -- predicting the next best move for growth, making every product a data product, or creating entirely new data-driven revenue streams.

How do market-leading organizations help data science teams do their best work within the constraints of the enterprise? Data science teams need fast access to business data and a diversity of tools for end-to-end machine learning that can make it challenging for IT to quickly enable them while maintaining data governance and controlling infrastructure costs. In this webinar, discover how Cloudera Machine Learning (CML), part of the new Cloudera Data Platform (CDP), brings the agility and economics of cloud to self-service data science on governed business data at scale. A consistent user-experience that doesn't change when the business moves data or infrastructure The webinar includes a live product demo that will highlight features for IT and end-users.

A would-be data management juggernaut got its first public airing as Cloudera -- a combination of formerly separate Hadoop pioneers Cloudera and Hortonworks -- as the newly stand-alone vendor's leaders publicly mapped the road it intends to take forward. "The combination has made sense for many years," said Tom Reilly, CEO of the combined companies, who held a similar role at the former Cloudera. Others agreed these leaders in open-source-oriented big data tooling -- built along lines drawn by big web companies, such as Google and Yahoo -- are better together than apart and can offer users a unified big data platform. Reilly spoke as part of a prerecorded webcast heralding the new company, which came after confirmation that shareholders of Cloudera and Hortonworks had approved a merger of the firms -- a deal first disclosed last October. Cloudera faces distinct challenges, as it moves data applications to the cloud and tries to convey users to the fast-growing new world of machine learning and AI.