At Geoblink we have a long history of deploying machine learning (ML) models behind REST APIs in order to make their predictions available to consumers. Lately we have been encountering other use cases where data teams find it useful to build their own APIs for a variety of tasks, so we decided to give a try to the new kid-in-the-block of the Python webdev framework ecosystem: FastAPI. In this blog post we’ll unveil our first impressions of FastAPI and discuss how we use it at Geoblink and, in particular, how this new framework has enabled us to version our APIs…
In this post I’ll discuss at a high-level why Geoblink chose Apache Airflow as its workflow management tool and give a brief introduction on its most relevant features.
Data pipelines at Geoblink cover a bunch of use cases such as:
- Downloading, cleaning and formatting sociodemographic data
- Spatial aggregation and disaggregation of sociodemographic and commercial indicators based on land registry data
- Processing large quantities of GPS or Telco data in order to estimate footfall footfall or population flows
- Retraining Machine Learning models
Managing those pipelines, most of them run periodically, can quickly become extremely costly to maintain. Data pipelines tend to…
Jordi Giner-Baldó
Data Scientist @ Geoblink
