Technology
Enterprise DataOps and ML platform engineering
- ML platform infrastructure
- Kubernetes
- Best practices implemented
- CI/CD
Challenge
A data software vendor needed a cloud-native data lake for their ML lab, with the DataOps practices, CI/CD and infrastructure to run it in production rather than in a notebook.
Solution
We designed the data lake architecture, built the ML pipeline infrastructure on Kubernetes, put the CI/CD practices in place, and deployed monitoring and alerting across the platform.
Key results
- Cloud data lake architecture designed and deployed
- ML pipeline infrastructure on Kubernetes
- DataOps culture established across engineering teams
- Automated data operations with full observability
Technologies
- Snowflake
- Databricks
- Kubernetes
- Terraform
- Python
- Spark
- MLFlow
What are you trying to fix?
Describe it in a paragraph. We'll tell you whether we are the right people, and what we would do first.