Digital Transformation Case Study

Saved ~$30,000 on monthly cloud costs.

Client

Client is revolutionizing the food industry using cutting-edge food innovation engine that combining data science and machine learning with biology and genetics.

Industry

Food Sciences & Crop Genetics.

Technology

Python, AWS (Cloud), GCP (Cloud), Docker, Terraform

Engagement Details

Service Type: Product Development Teams

Model: Offshore

Business Need

Automate and develop high volume data pipelines to fuel the AI engine for genetic data processing.

Challenges representation Icon

Challenges

  • Scattered Data sources across – Files (multiple formats), databases (SQL & NoSQL), Websites, FTPs & external APIs.
  • High Running costs on data pipelines on the cloud.
  • Slower processing of diverse genetic data on machine learning pipelines.

Services

  • Built a team of Data Architects, Data Engineers & Data Scientists with specific domain expertise on Food Sciences & Genetics.
  • Used in-house developed accelerator (Centipede) to automate the data aggregation and cleansing from multiple sources.
  • Reviewed existing architecture and created a step-by-step plan to migrate and improve the design to reduce cloud costs and improve data processing speeds.
  • Added a dedicated team to manage the MLOps and cloud infrastructure with 24×7 monitoring for production & Beta Stage environments.

Result

  • Reduced manual data cleansing and reduced data aggregation timelines.
  • Improved execution time of Machine Learning pipeline and reduced costs using a hybrid cloud architecture.
  • Saved ~$30,000 on monthly cloud costs.
  • Reduced Infrastructure management across clouds using IaC (Infrastructure as Code Terraform).
  • 24×7 MLOps & monitoring teams with minimum turnaround time.

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