Centipede-driven ingestion and ML pipeline tuning to cut cloud costs and speed up runs
A US-based food sciences organization needed to process high-volume genetic data from fragmented sources while controlling cloud run costs. CES automated ingestion and cleansing using its Centipede accelerator, reworked the data pipeline architecture, and introduced 24×7 MLOps monitoring—cutting monthly cloud spend by ~$30,000 and improving machine learning pipeline throughput.
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The Challenge
Fragmented genetic data sources
High cloud costs from inefficient pipelines
Slow ML pipeline throughput
the client
Food Sciences & Crop Genetics
Technology Stack
- Python
- AWS
- GCP
- Docker
- Terraform
- Centipede
Solution Area
- Data Engineering & Data Science | Data & Analytics
the impact
Automated data aggregation and cleansing
~$30,000/month cloud cost reduction
Faster ML pipeline execution
24×7 MLOps monitoring for Beta + Production
The shift was data-pipeline-led.
The result: faster processing at lower cloud cost.
The Need
The company needed high-volume pipelines to process genetic data at scale and support machine learning workflows. The existing setup pulled data from many source types and ran frequent large executions, which drove cloud costs up and slowed pipeline throughput. The goal was clear: automate data preparation, tune the architecture for cost and speed, and keep both Beta and Production stable with continuous monitoring.
Challenges
- Fragmented data sources and inconsistent formats: Data arrived through flat files in multiple formats, SQL/NoSQL databases, websites, FTPs, and external APIs—creating friction in ingestion and preparation.
- High cloud run costs during frequent large-scale processing: Pipeline executions carried a high monthly cost footprint, especially during repeated large runs.
- Slow ML pipeline throughput and limited operational visibility: ML pipelines took longer than acceptable due to ingestion and transformation bottlenecks, plus monitoring gaps across Beta and Production.
- Domain-aligned delivery team: Formed a product development team (data architects, data engineers, data scientists) with food sciences and genetics context.
- Centipede-based aggregation and cleansing: Used the Centipede accelerator to automate ingestion, normalization, validation, and cleansing across sources (files, FTPs, APIs, databases, websites).
- Architecture rework for hybrid cloud execution: Reviewed the existing setup and executed a step-by-step plan to reduce cloud run costs and improve processing speed through architectural changes and parallelized processing.
- Terraform IaC + MLOps coverage: Used Terraform to manage multi-cloud infrastructure and added a dedicated MLOps and cloud operations team with 24×7 monitoring for Beta and Production.
- Cut manual data cleansing and shortened data aggregation timelines.
- Improved ML pipeline execution time through parallelization and refactoring.
- Reduced multi-cloud infrastructure overhead through Terraform IaC.
- Reduced cloud run costs by ~$30,000 per month using a hybrid-cloud architecture approach.
- Put 24×7 monitoring in place for Beta and Production with faster turnaround during incidents.
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Take the Next Step
If genetic or scientific data is spread across too many sources and your pipeline spend keeps climbing, we can build high-volume ingestion, tune throughput, and put monitoring in place so runs stay predictable. Talk to our Strategic Client Partner
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