back_office_ops · workflow
Neoway builds ML platform with team-first approach and product thinking
Neoway's functional team structure created silos that prevented data scientists from accessing historical data in the proper format and from deploying model predictions integrated with the company's architecture; internal ML tooling suffered from poor developer experience and low user adoption.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Raw data pipeline preparation
Data pipelines prepare raw data from other applications into a structured format for data science tasks.
Tools used
AirflowGitlabDocker
Outcome
Neoway redesigned into cross-functional cells in early 2020 and built an ML platform covering data pipelines, a Feature Store, distributed development environments, model productization tooling, Airflow-based pipeline orchestration, and model serving APIs.
What failed first
The original features framework had terrible user experience and was abandoned; the functional organizational structure created dependency bottlenecks that slowed business value delivery.
Grounding & classification
Source type: technical build writeup
18 fields verified against source quotes, 1 dropped as unverifiable.
predictive analyticsknowledge basebuilder submittedfailure mode describednamed customerproduction runtime claimedtools describedworkflow describedfinancial servicessoftwareemployee productivitytechnical build writeupback office opsdata sync enrichment