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deepmind.google·6d ago
AI Engineering
Google Cloud Blog·May 28·5 min read
Evolving Dataflow to process massive datasets for machine learning
TL;DR
Machine learning at scale requires processing massive datasets for training and feature extraction, but uneven data distribution causes inefficiency. Google evolved Flume (MapReduce's successor) with liquid sharding (dynamic work-unit splitting) and global compute (cross-infrastructure scheduling) features now available in Dataflow.
✦ Why It Matters
Engineers can use Dataflow's liquid sharding and global compute to process massive ML datasets more efficiently with automatic load balancing.
Key Takeaways
- Utilize liquid sharding for better workload management in data pipelines.
- Implement TPU-aware autoscaling to optimize resource usage.
- Leverage unified batch and streaming for simplified data processing.
- Explore advanced developer tools for rapid prototyping and testing.
How It Works
Dataflow employs liquid sharding to dynamically adjust the distribution of work units during execution, ensuring efficient processing even with uneven data loads. This feature, combined with global compute capabilities, allows the platform to automatically allocate resources based on data locality and availability, enhancing overall performance.