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Data Curation and Scalable Infrastructure
Importance of Data Quality
- Customers often deal with massive amounts of raw aggregated data from various sources (e.g., sales transactions, customer interactions, IoT streams, image catalogs).
- Teams may not need all this raw data to answer business questions or solve analytical problems.
- The concept of "data curation" involves building and curating more specialized data sets for particular tasks, treating data as a product.
- This allows for more focused and usable data for researchers and product managers.
Performance Optimization
- Customers aim to saturate the available bandwidth between storage (e.g., Amazon S3) and compute to maximize performance.
- Storage can be a bottleneck, causing the entire end-to-end workload to be stalled and increasing compute costs.
- For Amazon S3, key performance metrics to optimize include request rate, throughput, and first-byte latency.
Scaling Storage
- Customers need to scale their storage to handle growing data volumes, from hundreds of objects to millions or billions.
- Services like Amazon S3 Intelligent Tiering can automatically optimize costs by moving data between access tiers based on changing access patterns.
Canva's Approach
- Canva is a collaborative online visual design tool with over 200 million monthly active users and 30 billion designs created.
- Canva has a mission to bring AI to the forefront of everything they do, with over 120 ML microservices.
- Challenges include content moderation, maintaining user trust and privacy, and curating data for various use cases.
- Canva has built a centralized moderation platform that leverages services like Amazon Rekognition to detect and flag unsafe content.
- Canva also has a platform to manage user privacy preferences and federate data access, ensuring compliance with regulations and respecting user consent.
Bria's Approach
- Bria is a generative AI platform for developers, training foundation models in the visual domain from scratch.
- Bria partners with data providers to obtain fully licensed data, then trains models and provides attribution and royalties back to the data providers.
- Bria's key challenges include scaling data pipelines to digest petabytes of multimodal data (images and videos) and building infrastructure to train models efficiently.
- Bria uses data pipelines with cheaper GPUs (e.g., NVIDIA A10) to preprocess and extract insights from the data, then leverages high-performance GPU clusters (e.g., NVIDIA H100) for model training.
- Bria utilizes Amazon S3, Amazon Glue, and Athena to build a scalable data catalog and attribution engine to track and report royalties to data providers.
Anthropic's Approach
- Anthropic is an AI research and safety company, known for their AI assistant Claude.
- Anthropic processes datasets of up to 200 petabytes every two weeks to train their large language models.
- Anthropic uses Amazon S3 features like S3 Express One Zone, cross-bucket replication, and S3 Intelligent Tiering to optimize performance, cost, and scalability.
- Anthropic has designed a structured approach to storing model checkpoints in S3, using a hierarchical key structure and lifecycle management to prune older checkpoints.
- Anthropic also leverages asynchronous I/O and the AWS Common Runtime S3 client to maximize throughput when ingesting data for training.
- Anthropic emphasizes the importance of designing key structures and naming conventions in S3 to enable future scalability and efficient data management.