• Artificial intelligence solutions
• What's new
• AI converged infrastructures
  • NetApp AIPod with NVIDIA DGX systems
    • Introduction
    • Hardware components
    • Software components
    • Architecture
    • Example deployment details
    • Validation and sizing guidance
    • Conclusion and additional information
  • NetApp AIPod with Lenovo for NVIDIA OVX
  • NVIDIA DGX SuperPOD with EF-Series
  • BeeGFS on NetApp with E-Series storage
  • Deploy IBM Spectrum Scale with E-Series storage
  • ONTAP and Lenovo ThinkSystem for AI
• MLOps and data management
  • Open Source MLOps with NetApp
    • Introduction
    • Technology overview
    • Architecture
    • NetApp Trident configuration
      • Trident backends for AIPod deployments
      • Kubernetes StorageClasses for AIPod deployments
    • Apache Airflow
      • Apache Airflow deployment
      • Use the NetApp DataOps Toolkit with Airflow
    • JupyterHub
      • JupyterHub deployment
      • Use the NetApp DataOps Toolkit with JupyterHub
      • Ingest data with NetApp SnapMirror
    • MLflow
      • MLflow deployment
      • Dataset-to-model traceability with NetApp and MLflow
    • Kubeflow
      • Kubeflow deployment
      • Provision Jupyter Notebook workspace
      • Use the NetApp DataOps Toolkit with Kubeflow
      • Train an image recognition model - example workflow
    • Example Trident operations
    • Example high-performance jobs for AIPod deployments
      • Execute a single-node AI workload
      • Execute a synchronous distributed AI workload
  • Hybrid MLOps with Domino Data Lab and NetApp
    • Introduction
    • Technology overview
    • Architecture
    • Initial setup
    • Expose existing NetApp volumes to Domino
    • Access the same data across different environments
    • Additional information
  • NVIDIA AI Enterprise with NetApp and VMware
    • Introduction
    • Technology overview
    • Architecture
    • Initial setup
    • Use NVIDIA NGC software
      • Setup
      • Use case example - TensorFlow Training Job
    • Additional information
  • FSx ONTAP for MLOps
    • Overview
    • Part 1 - Integrate Amazon FSx for NetApp ONTAP as a private S3 bucket into AWS SageMaker
    • Part 2 - Leverage Amazon FSx for NetApp ONTAP as a data source for model training in SageMaker
    • Part 3 - Build a simplified MLOps pipeline
  • StorageGRID data lake for autonomous driving
  • NetApp DataOps Toolkit
  • Vector database solution with NetApp
    • Overview
    • Introduction
    • Solution overview
    • Vector database
    • Technology requirement
    • Deployment procedure
    • Solution verification
      • Overview
      • Milvus cluster setup with Kubernetes in on-premises
      • Milvus with Amazon FSx ONTAP for NetApp ONTAP – file and object duality
      • Vector database protection using SnapCenter
      • Disaster recovery using SnapMirror
      • Performance validation
    • Vector database with Instaclustr using PostGreSQL: pgvector
    • Vector database use cases
    • Conclusion
    • Appendix A: values.yaml
    • Appendix B: prepare_data_netapp_new_py
    • Appendix C: verify_data_netapp.py
    • Appendix D: docker_compose.yml
• AI use cases
  • NetApp AIPod Mini for enterprise RAG
  • Responsible AI with Protopia image transformation
    • Overview
    • Solution areas
    • Technology overview
    • Test and validation plan
    • Test configuration
    • Test procedure
    • Inferencing accuracy comparison
    • Obfuscation speed
    • Conclusion
    • Additional information
  • Big data analytics to AI migration
  • Edge AI inferencing with NetApp and Lenovo
    • Introduction
    • Conclusion
  • Generative AI and NetApp value
  • Design Quantum StorNext with E-Series systems
  • Deploy Quantum StorNext with E-Series systems
• Modern data analytics
  • Cloud Data Management with NetApp File-Object Duality and AWS SageMaker
    • Solution overview
    • Solution technology
    • Data duality for data scientists and other applications
    • Conclusion
  • Apache Kafka workloads with NetApp NFS storage
    • Introduction
    • NetApp solution for silly rename issue in NFS to Kafka workload
    • Functional validation - Silly rename fix
    • Why NetApp NFS for Kafka workloads?
    • Performance overview and validation in AWS - Cloud Volume ONTAP
    • Performance overview and validation in AWS - FSx for NetApp ONTAP
    • Performance overview and validation with AFF on-premises
    • Conclusion
    • Where to find additional information
  • Confluent Kafka with NetApp ONTAP storage controllers
    • Overview
    • Solution
    • Technology overview
    • Confluent performance validation
    • Performance tests with produce-consume workload generator
    • Performance best practice guidelines
    • Conclusion
  • NetApp storage solutions for Apache Spark
    • Solution overview
    • Target audience
    • Solution technology
    • NetApp Spark solutions overview
    • Use cases summary
    • Major AI, ML, and DL use cases and architectures
    • Testing results
    • Hybrid cloud solution
    • Python scripts for each major use case
    • Conclusion
    • Where to find additional information
  • Big Data Analytics Data to Artificial Intelligence
    • Introduction
    • Customer challenges
    • Data mover solution
    • Data mover solution for AI
    • GPFS to NetApp ONTAP NFS
    • HDFS and MapR-FS to ONTAP NFS
    • Business benefits
    • GPFS to NFS - Detailed steps
    • MapR-FS to ONTAP NFS
    • Additional information
  • Best practices for Confluent Kafka
    • Introduction
    • Solution architecture details
    • Technology overview
    • Confluent verification
    • Performance tests with scalability
    • Confluent s3 connector
    • Confluent self-rebalancing clusters
    • Best practice guidelines
    • Sizing
    • Conclusion
  • NetApp hybrid cloud data solutions - Spark and Hadoop based on customer use cases
    • Solution overview
    • Data fabric powered by NetApp for big data architecture
    • Hadoop data protection and NetApp
    • Overview of Hadoop data protection use cases
    • Use case 1 - Backing up Hadoop data
    • Use case 2 - Backup and disaster recovery from the cloud to on-premises
    • Use case 3 - Enabling DevTest on existing Hadoop data
    • Use case 4 - Data protection and multicloud connectivity
    • Use case 5 - Accelerate analytic workloads
    • Conclusion
  • NetApp and Dremio's next generation hybrid iceberg lakehouse solution
    • Introduction
    • Solution overview
    • Technology requirements
    • Deployment procedure
    • Solution verification overview
    • Customer use cases
    • Conclusion
  • Different solutions for different analytics strategies
  • NetApp StorageGRID with Splunk SmartStore
    • Introduction
    • Solution overview
    • Benefits of this solution
    • Splunk architecture
    • StorageGRID Features for Splunk SmartStore
    • Tiering and cost savings
    • Single Site SmartStore Performance
    • Conclusion
  • NetApp E-Series E5700 and Splunk Enterprise
  • Deploy Apache Spark workload with NetApp storage
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