A leading vacation rental company needed a way to automate and standardize its machine learning infrastructure to establish repeatable processes across its organization. Many business units were handling unstructured data in different formats and figuring out the best way to classify, label and enrich these images of listings was a key priority in order to run effective data science initiatives.

The team sought to automate their ML pipelines in order to specifically reduce human labeling costs over time. To automate these pipelines, the company leveraged active learning and tightly integrated these workflows with their labeling operation in order to prioritize the right data to annotate. Across their multiple teams in departments such as Operations, Customer Support, Pricing, etc, the company now possesses a standard ML infrastructure that leverages a shared ontology to enable efficient classification and labeling. In addition, features such as Labelbox's Issues and Comments allow for more collaborative labeling and enables the team to create unique listings with rich metadata much faster than before.

After three months, the enterprises' ML pipelines are now fully automated where a majority of labels are model generated and accepted by a team of subject matter experts. Human labeling costs are now just a fraction of what they were when they first started, and over nine million annotation tasks have been completed within Labelbox.