Cloudera AI - Cloudera AI: Scale Machine Learning and AI with a Cloud-Native Platform
Cloudera AI, formerly Cloudera Machine Learning, offers a cloud-native platform that empowers enterprise data science teams to collaborate effectively, streamline AI development, and optimize machine learning workflows.
Cloudera AI Product Information
What is Cloudera AI?
Cloudera AI, known previously as Cloudera Machine Learning, allows data science teams to unleash the full potential of machine learning within their organization. By providing access to scalable compute resources, transparent and repeatable workflows, and a robust MLOps toolset, Cloudera AI accelerates the AI data lifecycle and enables successful collaboration across teams.
How to Use Cloudera AI?
With Cloudera AI, you can quickly deploy workspaces, gain full visibility across workflows, explore and visualize data sets, leverage accelerators for machine learning projects, and benefit from MLOps capabilities for seamless model deployment and monitoring. The platform supports cloud-native deployment across different environments, offering flexibility and scalability for AI initiatives.
Core Features of Cloudera AI
- Containerized workspaces
- SDX for training data & AI models
- Exploratory data science
- Accelerators for ML Projects (AMPs)
- Complete MLOps toolset
- Data visualization
Use Cases of Cloudera AI
- AI from concept to reality
- Scaling machine learning with MLOps
- Enabling exploratory data science
FAQ about Cloudera AI
What are AMPs in Cloudera AI?
AMPs, or Accelerators for ML Projects, are pre-built reference examples designed to jumpstart AI initiatives and reduce time to value for machine learning projects.
What deployment options does Cloudera AI offer?
Cloudera AI provides cloud-native deployment options, allowing deployment anywhere with a consistent experience across different environments, including multi-cloud and on-premises deployment.
Can Cloudera AI handle model deployment and monitoring?
Yes, Cloudera AI offers MLOps capabilities, enabling one-click model deployment, model cataloging, and granular prediction monitoring to ensure secure and accurate models across production environments.
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