Common pipelines, standardized for efficiency

ML Workflow Template Library

An interactive worksheet library capturing common ML workflows for training, inference, and evaluation. Use these templates to accelerate development, ensure repeatability, and support standardization across ML ops teams.

Managing machine learning workflows involves recurring pipelines for training, inference, and evaluation. Standardizing these pipelines reduces operational complexity and improves reproducibility across teams.

This interactive template library provides gated, customizable worksheets for common ML workflows. Each template guides users through key inputs and parameters, enabling rapid workflow configuration and documentation.

Inputs

Select workflow type

Data source URI

Enter the URI or path to the input dataset (e.g., s3://bucket/dataset).

Compute target

Define the compute environment or cluster where the workflow will execute.

Model architecture

Hyperparameters

Optional: JSON or key=value pairs for training parameters (learning rate, epochs, etc.)

Model artifact output location

Where to store the trained model or inference output.

Metrics for evaluation

List quantitative metrics (accuracy, F1-score) for evaluation workflows.

Result

Estimated workflow runtime

if workflow-type == 'training' then (epochs * dataset_size / 10000) else if workflow-type == 'inference' then (dataset_size / 1000) else (dataset_size / 5000)

—

Estimated data throughput

dataset_size / estimated-runtime

—

Workflow summary

Best practice

Use consistent model artifact naming conventions and metric tracking systems to integrate seamlessly with MLOps monitoring solutions.

Subsequent sections unlock after submit