Cloudera Machine Learning

Basic Information

Cloudera Machine Learning (CML) is a platform designed for the end-to-end machine learning lifecycle, from data engineering to model deployment and governance. It is a Kubernetes-based service that is part of the Cloudera Data Platform (CDP). CML provides a unified space for collaborative data science solutions, leveraging ML-relevant, native tools. It is available for deployment on both private and public cloud environments, supporting hybrid scenarios.

• Model: Cloudera Machine Learning (CML)
• Version: ML Runtimes Version 2024.02.1 (latest observed in search results for pre-installed packages)
• Release Date: Continuously updated as part of Cloudera Data Platform. Specific release dates for major versions are not consistently available, but ML Runtimes are versioned (e.g., 2023.12.1, 2023.08.2, 2024.02.1).
• Minimum Requirements: Varies significantly based on deployment model (ECS or OCP) and expected user workloads. Generally requires substantial CPU, memory, and storage.
• Supported Operating Systems: Primarily runs on Red Hat OpenShift Container Platform (OCP) versions 4.10 or 4.8 (for upgrades to 1.5.0) and Embedded Container Service (ECS).
• Latest Stable Version: ML Runtimes Version 2024.02.1 (as of latest available documentation snippets).
• End of Support Date: End of Support (EoS) dates for ML Runtime Variants are typically 6 months after their End of Maintenance (EoM) dates, which align with the variant's kernel's upstream security support. For Spark runtime addons, certification for Spark 2.4, 3.2, and 3.3 ends against Public Cloud Data Lakes running versions higher than 7.2.18, with users encouraged to migrate to Spark 3.5 for long-term support beyond 7.2.18.
• End of Life Date: Not explicitly stated as a single date for the entire product, but tied to the support lifecycle of its underlying components and runtime variants.
• Auto-update Expiration Date: Not specified.
• License Type: Commercial, likely subscription-based as part of Cloudera Data Platform.
• Deployment Model: On-premises (via Embedded Container Service or OpenShift Container Platform) and Cloud (Public Cloud Data Lakes), supporting hybrid and multi-cloud environments.

Technical Requirements

Cloudera Machine Learning's technical requirements are substantial, reflecting its role as an enterprise-grade platform for large-scale data processing and machine learning. Requirements vary based on the deployment method (Embedded Container Service or OpenShift Container Platform) and the scale of workloads.

• RAM:
  • Minimum per workspace: 128 GB.
  • Recommended per workspace: 256 GB.
  • Additional per concurrent user workload: Minimum 2 GB, Recommended 4-64 GB (dependent on use cases).
• Processor:
  • Minimum per workspace: 32 Cores.
  • Recommended per workspace: 32-48 Cores.
  • Additional per concurrent user workload: Minimum 1 Core, Recommended 2-16 Cores (dependent on use cases).
• Storage:
  • For ECS: Minimum 600 GB SSDs, Recommended cumulative 4500 GB of Block storage for project use.
  • For OCP: 4 TB of persistent volume block storage per ML Workspace, 1 TB of NFS space recommended per Workspace.
  • For production environments, an External NFS environment with at least 1000 GB of NFS storage is strongly recommended.
  • Monitoring volume: 60 GB recommended.
  • VFS storage can use Longhorn NFS-provisioner or directly connect to NFS.
• Display: Not explicitly specified, as it is a platform accessed via web interfaces.
• Ports: Network bandwidth of 1GB/s to all nodes and the base cluster is required. Specific port requirements for internal services and external access (e.g., for REST APIs, SSH tunnels) are implied but not detailed in general requirements.
• Operating System: Red Hat OpenShift Container Platform (OCP) versions 4.10 or 4.8, or Embedded Container Service (ECS).

Analysis: The technical requirements highlight CML's enterprise focus, demanding significant computational and storage resources. The platform is designed for scalability, with resource allocation scaling with the number of workspaces and concurrent user workloads. The emphasis on dedicated block storage and external NFS for production environments underscores the need for robust, high-performance data persistence. The reliance on Kubernetes (via OCP or ECS) indicates a cloud-native architecture, requiring a well-configured container orchestration environment. The resource recommendations are substantial, suggesting that CML is best suited for organizations with significant data science needs and infrastructure capabilities.

Support & Compatibility

Cloudera Machine Learning is designed for broad compatibility within the Cloudera Data Platform ecosystem and supports various open-source machine learning tools and frameworks.

• Latest Version: ML Runtimes Version 2024.02.1 (as of latest available documentation snippets).
• OS Support: Red Hat OpenShift Container Platform (OCP) versions 4.10 or 4.8, and Embedded Container Service (ECS).
• End of Support Date: End of Support (EoS) for ML Runtime Variants is typically 6 months after their End of Maintenance (EoM) dates, which are aligned with the variant's kernel's upstream security support. Specific Spark runtime addons (2.4, 3.2, 3.3) will end certification against Public Cloud Data Lakes running versions higher than 7.2.18, with migration to Spark 3.5 recommended for long-term support.
• Localization: Not explicitly detailed in available information, but enterprise platforms typically offer multi-language support.
• Available Drivers: CML integrates with various ML runtimes and libraries, including Python (3.7, 3.8, 3.9, 3.10, 3.11), R (3.6, 4.0, 4.1), and Scala (2.11). It supports popular ML frameworks like TensorFlow, Scikit-learn, and PyTorch.

Analysis: Cloudera Machine Learning demonstrates strong compatibility with leading container orchestration platforms (OpenShift, Kubernetes via ECS) and a wide array of popular data science programming languages and libraries. The modular nature of ML Runtimes allows for flexibility in supporting different versions of Python, R, and Scala, along with their respective ecosystems. The support lifecycle policy, tied to upstream security support for kernel variants, provides a clear framework for maintenance and upgrades. However, users must be aware of specific end-of-support notices for particular runtime components, such as older Spark versions, and plan migrations accordingly to maintain long-term support.

Security Status

Cloudera Machine Learning integrates robust security features as part of the broader Cloudera Data Platform, emphasizing governed and compliant AI workflows.

• Security Features: Data governance and security are built into the platform. It supports secure, governed access to data and compute for AI applications.
• Known Vulnerabilities: Not explicitly detailed in general product overviews, but Cloudera's support policy indicates that critical security fixes (CVE score of 9.0 or higher) may be provided during the End of Maintenance period.
• Blacklist Status: No information found indicating a blacklist status.
• Certifications: Not explicitly detailed in available information, but enterprise platforms typically adhere to industry compliance standards.
• Encryption Support: Implied through general enterprise security practices, but specific encryption protocols are not detailed in available public information.
• Authentication Methods:
  • Internal local database.
  • External services: Active Directory, OpenLDAP-compatible directory services, SAML 2.0 Identity Providers.
  • Cloudera recommends leveraging Single Sign-On (SSO) via the CDP management console.
  • Supports Hadoop authentication, including Kerberos, for Spark workloads and Impala connections.
  • Cloudera AI Inference service uses Cloudera Workload Authentication JSON Web Token (JWT).
• General Recommendations: Cloudera emphasizes secure, governed, and compliant AI workflows across the entire AI lifecycle. It provides tools for building, scaling, and securing AI, ensuring sensitive data and models remain private with end-to-end governance.

Analysis: Cloudera Machine Learning offers a comprehensive security framework, particularly through its integration with the Cloudera Data Platform. The support for various authentication methods, including enterprise standards like Active Directory, LDAP, and SAML 2.0, ensures flexible and secure user access. The use of JWT for the Inference service further strengthens API security. While specific certifications and detailed encryption protocols are not broadly publicized, the platform's focus on data governance and secure AI workflows suggests a strong commitment to enterprise security. Users should follow Cloudera's recommendations for SSO and Kerberos authentication to maximize security posture.

Performance & Benchmarks

Cloudera Machine Learning is designed for scalable and high-performance machine learning workloads, leveraging distributed computing.

• Benchmark Scores: Specific public benchmark scores for Cloudera Machine Learning are not readily available in the provided search results.
• Real-world Performance Metrics:
  • Scales effortlessly to handle massive data volumes and deploy ML models at scale.
  • Distributes machine learning workloads across multiple nodes using tools like Apache Spark and Dask, ensuring scalability for big data projects.
  • Enables real-time processing and predictive analytics.
  • Optimized for running machine learning workloads and complex analytics quickly.
  • Supports serverless autoscaling for intermittent workloads.
• Power Consumption: Not explicitly detailed.
• Carbon Footprint: Not explicitly detailed.
• Comparison with Similar Assets:
  • Competitors include Dataiku, DataRobot, Amazon SageMaker, Alteryx Designer, MATLAB, Altair RapidMiner, IBM SPSS Statistics, and Databricks Data Intelligence Platform.
  • Cloudera AI is rated higher than Alibaba Cloud Machine Learning Platform for AI in service, support, integration, deployment, evaluation, and contracting.
  • Cloudera AI is rated higher than IBM SPSS Statistics in service, support, evaluation, and contracting.
  • Compared to Databricks, Cloudera (CDP) is a veteran solution rooted in Hadoop, offering a comprehensive stack for complex data ecosystems, while Databricks, born in the cloud, champions the "lakehouse" paradigm with an AI-optimized engine for ML workloads.
  • Cloudera's Impala is known for low-latency SQL, while Databricks' engine is optimized for ML and complex analytics, supporting serverless autoscaling.
  • Alternatives for specific use cases include Snowflake (scalability), BigQuery (real-time analysis), Databricks (machine learning), AWS EMR (flexibility), Amazon Redshift (cost-effectiveness), and Oracle Exadata (high performance).

Analysis: Cloudera Machine Learning is engineered for high performance and scalability, particularly in environments dealing with large, complex datasets. Its ability to distribute workloads using Apache Spark and Dask, coupled with serverless autoscaling, ensures efficient resource utilization and rapid execution of ML tasks. While direct benchmark scores are not provided, user feedback and comparisons with competitors suggest strong capabilities in enterprise settings, especially for organizations with existing Hadoop ecosystems. The platform's strengths lie in its comprehensive integration within the Cloudera Data Platform, offering a unified environment for data management, analytics, and machine learning.

User Reviews & Feedback

User reviews and feedback for Cloudera Machine Learning (often referred to as Cloudera AI) highlight its strengths in enterprise data management and its comprehensive platform capabilities.

• Strengths:
  • Advanced data science platform for analyzing, visualizing, and modeling data.
  • Integrated suite of powerful tools and services for building and deploying machine learning capabilities.
  • Intuitive drag-and-drop interface, pre-trained models, and extensive algorithm library.
  • Flexible, scalable, and secure environment for data exploration and experimentation.
  • Enables organizations to handle massive data volumes and deploy ML models at scale.
  • Fosters collaboration among data scientists, engineers, and business analysts.
  • Increases productivity through automated machine learning and pre-built workflows.
  • Ease of use and speed in achieving measurable impact.
  • Very useful tool to collaborate in data science projects.
• Weaknesses:
  • Can be complex to use and requires a significant learning curve for some users.
  • Potentially costly, especially for organizations with large amounts of data, and the pricing structure for enterprises can be high.
  • Migration to Cloudera can be difficult due to its complete distribution package.
  • Limited support for cloud in some aspects (though it supports hybrid/multi-cloud deployments).
• Recommended Use Cases:
  • Building and deploying ML models quickly.
  • Leveraging big data to drive decisions.
  • Fraud detection and prevention (e.g., in financial services, healthcare).
  • Risk assessment and portfolio management in finance.
  • Predictive models for improving patient outcomes and managing healthcare resources.
  • Automated visual inspection and detection of surface imperfections in manufacturing.
  • Predictive maintenance.
  • Remote monitoring and control, especially for real-time data streaming.
  • End-to-end MLOps pipelines, including automated CI/CD, model registry, and deployment capabilities.

Analysis: Cloudera Machine Learning is highly regarded for its comprehensive capabilities in managing the entire ML lifecycle within an enterprise context. Users appreciate its scalability, security, and collaborative features, which are crucial for large data science teams. The platform's ability to integrate with various open-source tools and provide pre-built workflows significantly boosts productivity. However, the complexity and potential cost are noted as challenges, suggesting that it is best suited for organizations with dedicated resources and a clear need for an integrated, scalable ML platform. Its strong performance in specific industry use cases like finance, healthcare, and manufacturing further solidifies its value.

Summary

Cloudera Machine Learning (CML) is a robust, enterprise-grade platform designed to facilitate the entire machine learning lifecycle, from data ingestion and engineering to model training, deployment, and governance. As a Kubernetes-based service within the Cloudera Data Platform (CDP), it offers a unified and collaborative environment for data scientists and engineers. CML supports flexible deployment models, including on-premises (via Embedded Container Service or OpenShift Container Platform) and cloud, catering to hybrid and multi-cloud strategies.

The platform's strengths lie in its exceptional scalability, enabling organizations to manage massive data volumes and deploy machine learning models at an enterprise scale. It leverages distributed computing technologies like Apache Spark and Dask to optimize workload distribution and ensure high performance. CML provides a rich ecosystem of supported runtimes, including various versions of Python, R, and Scala, along with popular ML frameworks such as TensorFlow, Scikit-learn, and PyTorch. Its built-in security features, including support for Active Directory, LDAP, SAML 2.0, and Kerberos authentication, ensure governed and compliant AI workflows, protecting sensitive data throughout the ML lifecycle.

However, CML presents significant technical requirements, demanding substantial CPU, RAM, and storage resources, particularly for production environments. This indicates that the platform is best suited for large enterprises with the infrastructure and expertise to manage complex big data and ML operations. User feedback highlights its power and effectiveness for collaborative data science projects and its ability to accelerate time to insight. Conversely, some users note its complexity and potentially high cost as areas of concern, suggesting a learning curve and a need for careful cost-benefit analysis.

Overall, Cloudera Machine Learning is a powerful solution for organizations committed to operationalizing AI at scale, especially those already invested in the Cloudera ecosystem or requiring robust on-premises and hybrid cloud capabilities. Its comprehensive feature set, strong security, and performance make it a strong contender for complex, data-intensive machine learning use cases across various industries like finance, healthcare, and manufacturing.

The information provided is based on publicly available data and may vary depending on specific device configurations. For up-to-date information, please consult official manufacturer resources.