MLOps Work Flow Slide

Description

Explain your end-to-end machine learning workflow with a clear, presentation-ready pipeline. The slide maps each stage—Data Ingestion, Split, Data Preparation & Feature Engineering, Model Training, Validation, Evaluation (Test), Register & Deploy, Serve Predictions, and Monitor & Feedback—using left-to-right flow blocks. Three gear graphics emphasize the core learning loop (training–validation–model), while connecting arrows show iteration and retraining triggers. Notes call out batch/real-time serving, drift and latency monitoring, and feedback to data refresh. A foundation bar highlights essential MLOps capabilities: versioning, experiment tracking, CI/CD, model registry, and governance. The dark theme with soft shadows ensures excellent contrast, and every element is fully editable—resize shapes, change colors, swap icons, or adapt labels to your stack. Use it to brief executives, align data science and engineering teams, or document your production path from data to value.

Who is it for

Data scientists, ML engineers, analytics leaders, and product managers who must communicate pipelines to technical and business stakeholders. Great for center-of-excellence teams, analytics translators, and consultants standardizing process visuals across projects.

Other Uses

Repurpose the pipeline for A/B testing flows, feature store rollouts, real-time inference architectures, or compliance reviews. Add metrics to each stage to build an MLOps dashboard, attach owners and SLAs for operations, or duplicate the canvas to compare current vs. target pipelines.