Organizations deploying ML models at scale without proper MLOps practices face model quality and continuity issues and lack of oversight, while data science teams waste time on data preparation rather than core model development, with brittle production deployments.
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 · ML model development
Data scientists apply ML on prepared data to identify the best-performing model for the given task.
A leading financial services institution using Dataiku achieved an 86% reduction in time spent optimizing model code for production, 75% less pipeline production code written by ML engineers, and a 90% reduction in overall time to deployment, while supporting more than 125 stakeholders with mission-critical workloads.
What failed first
Prior desktop solutions resulted in significantly slower deployments, and ML engineers spent excessive time writing pipeline production code and optimizing model code for production.