Who should use the Experiment Tracking workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Development
Practical execution plan for experiment tracking with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
A production-ready model versioned and deployed, with full traceability back to the experiment.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A production-ready model versioned and deployed, with full traceability back to the experiment.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Zed 1.0 to a versioned, self-contained configuration that can be reused or shared to reproduce the experiment. Then, you pass the output to MLflow to training code that automatically records all relevant metrics and artifacts to a central dashboard. Then, you pass the output to MLflow to real-time visibility into experiment progress, enabling quick decisions and early termination of poor runs. Then, you pass the output to MLflow to a clear, data-driven selection of the best model configuration, with visual evidence for stakeholders. Then, you pass the output to Neptune.ai to a fully documented, archived experiment that can be revisited or reproduced by anyone on the team. Finally, Polyaxon is used to a production-ready model versioned and deployed, with full traceability back to the experiment.
Define Experiment Configuration
A versioned, self-contained configuration that can be reused or shared to reproduce the experiment.
Instrument Training Code with Logging
Training code that automatically records all relevant metrics and artifacts to a central dashboard.
Execute and Monitor Runs
Real-time visibility into experiment progress, enabling quick decisions and early termination of poor runs.
Compare and Analyze Results
A clear, data-driven selection of the best model configuration, with visual evidence for stakeholders.
Archive and Document the Experiment
A fully documented, archived experiment that can be revisited or reproduced by anyone on the team.
Promote Model to Production (Optional)
A production-ready model versioned and deployed, with full traceability back to the experiment.
Set up a structured configuration file (YAML/JSON) that captures all hyperparameters, dataset paths, model architecture choices, and environment details. This ensures every run is reproducible and comparable. Use a version control system to track changes to the config over time.
Why Zed 1.0: Zed 1.0 provides code editing with syntax highlighting and autocompletion for YAML/JSON, integrated Git version control, and terminal access for conda/pip environment management.
Integrate an experiment tracking library (e.g., MLflow, Weights & Biases, or TensorBoard) into the training script. Log key metrics (loss, accuracy, F1) at each epoch, and record hyperparameters and model checkpoints. Use callbacks or decorators to automate logging without cluttering the core logic.
Why MLflow: MLflow directly provides experiment tracking and logging capabilities needed for instrumenting training code.
Run the experiment locally or on a remote cluster, monitoring real-time metrics via the tracking tool's dashboard. Compare live runs side-by-side to spot divergences early. If a run fails, the logged config and partial metrics help diagnose the issue without restarting from scratch.
Why MLflow: MLflow provides a built-in experiment tracking UI dashboard for monitoring runs.
After multiple runs complete, use the tracking tool's comparison features to plot metrics across runs (e.g., loss curves, accuracy vs. hyperparameters). Identify the best-performing configuration and any surprising correlations. Export tables and charts for reporting.
Why MLflow: MLflow includes experiment comparison tools in its UI for analyzing results across runs.
Finalize the experiment by archiving all artifacts (config, logs, model weights, plots) in a shared location (e.g., cloud storage or Git LFS). Write a brief summary documenting the goal, key findings, and the champion run ID. This ensures the experiment is reproducible and understandable months later.
Why Neptune.ai: Neptune.ai provides artifact storage, experiment documentation, and model versioning for archiving.
If the experiment yields a production-ready model, register it in a model registry (e.g., MLflow Model Registry) with versioning and stage transitions. Deploy the model to a serving endpoint or export it for edge devices. This step closes the loop from experiment to business value.
Why Polyaxon: Polyaxon supports model deployment and experiment tracking, suitable for promoting models to production.
§ Before you start
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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