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Conceptual guidance for designing and operating architectures that enable machine learning models to scale with growing data and user demand.
Scaling AI Systems provides guidance for architectures and operational practices that let machine learning models train and serve under growing data and traffic. It covers distributed training, model parallelism, efficient inference serving, data pipelines, monitoring and autoscaling. It highlights trade-offs between cost, latency and model accuracy for production ML.
P99 latency measures the upper bound of response times and is critical for SLA monitoring.
Indicates how many inference requests can be processed per second.
Time or resource consumption until a model reaches the desired accuracy.
Ray was used to train a large BERT model across multiple GPU nodes, significantly reducing training time.
A text-to-speech API autoscaled based on P99 latency and GPU utilization to optimize costs.
Tenant isolation and QoS policies enabled parallel hosting of different models on a shared platform.
Analyze workloads and define performance goals.
Select and provision infrastructure and orchestration platform.
Integrate distributed training solutions and inference tooling.
Introduce monitoring, autoscaling policies and cost monitoring.