Cloudera Machine Learning Overview
Cloudera Machine Learning (CML) is Cloudera’s new cloud-native machine learning service, built for CDP. The CML service provisions clusters, also known as ML workspaces, that run natively on Kubernetes.
Each ML workspace enable teams of data scientists to develop, test, train, and ultimately deploy machine learning models for building predictive applications all on the data under management within the enterprise data cloud. ML workspaces are ephemeral, allowing you to create and delete them on-demand. ML workspaces support fully-containerized execution of Python, R, Scala, and Spark workloads through flexible and extensible engines.
Seamless portability across private cloud, public cloud, and hybrid cloud powered by Kubernetes
Rapid cloud provisioning and autoscaling
Fully containerized workloads - including Python, R, and Spark-on-Kubernetes - for scale-out data engineering and machine learning with seamless distributed dependency management
High performance deep learning with distributed GPU scheduling and training
Secure data access across HDFS, cloud object stores, and external databases
The cloud offers many advantages for unpredictable and heterogeneous workloads, but there are two challenges: 1) data is often spread across multiple clouds and on-premises systems, and 2) existing products only cover parts of the machine learning lifecycle. Cloudera Machine Learning directly addresses both these issues. It’s built for the agility and power of cloud computing, but isn’t limited to any one provider or data source. And it is a comprehensive platform to collaboratively build and deploy machine learning capabilities at scale. CML gives you the power to transform your business with machine learning and AI.
- Data management and data science executives at large enterprises who want to empower teams to develop and deploy machine learning at scale.
- Data scientist developers (use open source languages like Python, R, Scala) who want fast access to compute and corporate data, the ability to work collaboratively and share, and an agile path to production model deployment.
- IT architects and administrators who need a scalable platform to enable data scientists in the face of shifting cloud strategies while maintaining security, governance and compliance. They can easily provision environments and enable resource scaling so they - and the teams they support - can spend less time on infrastructure and more time on innovation.