Scalable Data Storage and Processing for AI Workloads
1h 30mIntermediate2025-03-17
Authors
Janani Ravi
Certified Google Cloud Architect and Data Engineer
Course details
Solutions for data storage and processing are essential, but how do you manage them effectively at scale? In this course, instructor Janani Ravi covers the fundamentals of designing and implementing data storage systems that can efficiently handle the large-scale demands of AI-powered applications. Explore techniques for managing, processing, and optimizing data flow in distributed environments to ensure high-performance AI model execution. An ideal fit for tech professionals working with AI, data infrastructure, and machine learning operations, this course equips you with the skills you need to not only manage but also optimize your AI workloads.
Skills covered
Cloud StorageData EngineeringArtificial Intelligence FoundationsArtificial Intelligence (AI)Cloud ComputingData ScienceOne-Off
Concepts
0. Introduction
- 01 - Scalable solutions for storage and processing
- 02 - Prerequisites
1. Types of Data and Storage Requirements
- 03 - Types of data - Structured, semistructured, and unstructured
- 04 - Understanding structured data
- 05 - Understanding semistructured data
- 06 - Understanding unstructured data
2. Data Storage in the AI Pipeline
- 07 - Storage requirements in the AI pipeline
- 08 - Data storage in the AI workflow
- 09 - AI storage considerations
- 10 - AI storage best practices
- 11 - Cloud storage on Google Cloud
- 12 - Object storage with Amazon S3
- 13 - Blob storage on Azure
3. Vector Databases and RAGs
- 14 - Retrieval-augmented generation
- 15 - Vector databases and embeddings
- 16 - Semantic search with Pinecone
4. AI Workloads and Processing
- 17 - Types of AI workloads
- 18 - Best practices to optimize AI workloads
- 19 - AI workloads on the cloud
Conclusion
- 20 - Summary and next steps