Storage for AI and ML Workloads Essentials Training by Tonex
Storage for AI and ML Workloads Training by Tonex explores storage optimization for AI/ML workloads. It covers high-performance data pipelines, accelerated processing with GPUs and TPUs, and managing unstructured data effectively. Participants will gain insights into building robust storage solutions for AI/ML projects, ensuring scalability and efficiency. Learn best practices for storage design, data handling, and integration with AI/ML workflows. This course is designed for professionals seeking to enhance their knowledge in storage technologies for advanced AI/ML applications.
Audience:
AI/ML professionals, data scientists, IT managers, storage architects, and system engineers.
Learning Objectives:
- Understand storage requirements for AI/ML workloads.
- Learn to optimize data pipelines for performance.
- Explore GPU and TPU integration for storage tasks.
- Manage unstructured data for effective training.
- Develop scalable storage solutions.
- Gain insights into advanced storage strategies.
Course Modules:
Module 1: Introduction to AI/ML Storage
- Overview of AI/ML data types.
- Understanding storage requirements.
- Basics of data pipelines.
- Key challenges in AI/ML storage.
- Role of GPUs and TPUs in storage.
- Importance of scalability.
Module 2: Optimizing Data Pipelines
- Strategies for high-speed data movement.
- Reducing bottlenecks in pipelines.
- Tools for pipeline optimization.
- Integrating pipelines with storage systems.
- Monitoring and troubleshooting.
- Ensuring data integrity.
Module 3: Accelerated Processing with GPUs and TPUs
- Understanding GPU and TPU architecture.
- Selecting hardware for storage tasks.
- Data storage strategies for GPU/TPU use.
- Performance tuning for accelerators.
- Integration with AI/ML frameworks.
- Troubleshooting GPU/TPU challenges.
Module 4: Managing Unstructured Data
- Characteristics of unstructured data.
- Storage solutions for unstructured formats.
- Organizing large datasets effectively.
- Metadata management best practices.
- Tools for unstructured data analysis.
- Security and compliance considerations.
Module 5: Designing Scalable Storage Systems
- Principles of scalability.
- Selecting storage architecture.
- Building redundant systems.
- Cloud-based storage solutions.
- Balancing cost and performance.
- Future-proofing storage infrastructure.
Module 6: Advanced Storage Strategies
- Hybrid storage approaches.
- Leveraging edge storage.
- AI-driven storage management.
- Optimizing storage for real-time AI.
- Case studies and best practices.
- Emerging trends in AI/ML storage.
Enhance your expertise in AI/ML storage solutions with Tonex. Enroll now to build cutting-edge, scalable systems for your organization!