Billion-Row Batch Inference for Scientific Models

A production TensorFlow prediction pipeline processed more than ten billion rows end to end in under three hours, so scientific teams could evaluate candidates and iterate faster.

Rows
10B+
End to end
Under 3 hours
How the billion-row batch inference pipeline runs More than ten billion rows move through preprocessing, a set of TensorFlow models, and materialized outputs on the shared Kubernetes, Ray, and Flyte platform, in under three hours end to end. Input rows (10B+ per run) flows to Preprocessing (Input pipeline) [10B+ rows]; Preprocessing (Input pipeline) flows to TensorFlow models (Batch prediction) [batches]; Shared ML platform (K8s · Ray · Flyte) drives Preprocessing (Input pipeline) [scales]; Shared ML platform (K8s · Ray · Flyte) drives TensorFlow models (Batch prediction) [runs]; TensorFlow models (Batch prediction) flows to Predictions (Materialized); TensorFlow models (Batch prediction) drives Prometheus (Grafana · alerting); Predictions (Materialized) flows to Scientific teams (Candidate review) [results]. Owned by Sudhanva: Preprocessing, Shared ML platform, TensorFlow models and Predictions. Outcomes: End-to-end run: Under 3 hours. 10B+ PER RUN Input rows INPUT PIPELINE Preprocessing K8S · RAY · FLYTE Shared ML platform BATCH PREDICTION TensorFlow models MATERIALIZED Predictions GRAFANA · ALERTING Prometheus CANDIDATE REVIEW Scientific teams 10B+ rows batches scales runs results END-TO-END RUN Under 3 hours Owned by Sudhanva Data Control
How the billion-row batch inference pipeline runs 10B+ PER RUN Input rows INPUT PIPELINE Preprocessing K8S · RAY · FLYTE Shared ML platform BATCH PREDICTION TensorFlow models MATERIALIZED Predictions GRAFANA · ALERTING Prometheus CANDIDATE REVIEW Scientific teams 10B+ rows batches runs results END-TO-END RUN Under 3 hours Owned by Sudhanva Data Control
More than ten billion rows move through preprocessing, a set of TensorFlow models, and materialized outputs on the shared Kubernetes, Ray, and Flyte platform, in under three hours end to end.

Confidentiality note. Employer-specific implementation details, internal names, and proprietary scientific context have been intentionally generalized. The scale, responsibilities, technologies, and outcomes below are limited to facts already published in Sudhanva’s résumé and professional profile.

Problem

Scientific model evaluation required running TensorFlow predictions over data volumes too large for a notebook-oriented workflow. The system needed predictable throughput, repeatable execution, and outputs that downstream teams could use without manual intervention.

Scale and constraints

Scale

  • More than ten billion input rows per production run.
  • End-to-end processing completed in under three hours.
  • Multiple TensorFlow models participated in the prediction workflow.

Constraints

  • Keep model execution and the surrounding data pipeline efficient at large scale.
  • Produce results that stay repeatable from run to run for scientific evaluation.
  • Operate inside the reliability and cost boundaries of a shared production ML platform.

Sudhanva’s ownership

Sudhanva built the batch prediction pipelines and owned the production ML infrastructure they ran on.

  • End-to-end batch prediction workflow design and implementation.
  • TensorFlow execution and input-pipeline performance work.
  • Operational integration with the shared Kubernetes, Ray, and Flyte platform.

Architecture at a safe level

This public architecture describes system responsibilities and leaves out internal service names and proprietary topology. The relevant boundaries were workload execution, orchestration, data movement, observability, and delivery.

  • Measured and optimized preprocessing, model execution, and output materialization together as one pipeline.
  • Used distributed platform capabilities to scale data movement and model execution while keeping runs reproducible.
  • Instrumented the production path so runtime and resource behavior could be observed and improved.

Engineering decisions and tradeoffs

  • Throughput improvements had to preserve scientific correctness and repeatability.
  • Resource allocation balanced faster completion against shared-cluster cost and capacity.

Results

  • Processed more than ten billion rows in under three hours.
  • Accelerated drug-candidate evaluation and experimental iteration.

Reliability and operations

  • Production runs were observable through the platform monitoring and alerting stack.
  • Repeatable orchestration reduced reliance on manual notebook execution.

Transferable lessons

  • For large batch inference, data loading and preprocessing deserve the same attention as model compute.
  • End-to-end runtime is the useful product metric; isolated accelerator utilization is only one input.

Technologies

  • TensorFlow
  • Kubernetes
  • Ray
  • Flyte
  • Python
  • Prometheus
  • Grafana

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