Who should use the MLOps Workflow with Polyaxon workflow?
Teams or solo builders working on data science & ml tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Data Science & ML
Automate machine learning lifecycle from experiment tracking to model deployment using Polyaxon on Kubernetes.
Deliverable outcome
Model retraining and redeployment are fully automated with CI/CD, ensuring continuous improvement.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
Model retraining and redeployment are fully automated with CI/CD, ensuring continuous improvement.
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 Huddle01 Cloud to polyaxon is running on kubernetes, accessible to the team with persistent storage configured. Then, you pass the output to Polyaxon to multiple ml experiments are tracked with full reproducibility (code, data, params, metrics) in polyaxon. Then, you pass the output to Polyaxon to optimal hyperparameters identified with automated search, tracked alongside all experiments. Then, you pass the output to Polyaxon to model is versioned, documented, and traceable to its training experiment in the registry. Then, you pass the output to Polyaxon to model is live as a scalable, monitored api endpoint on kubernetes. Finally, GitLab is used to model retraining and redeployment are fully automated with ci/cd, ensuring continuous improvement.
Set up Polyaxon on Kubernetes cluster
Polyaxon is running on Kubernetes, accessible to the team with persistent storage configured.
Define and run ML experiments with Polyaxon
Multiple ML experiments are tracked with full reproducibility (code, data, params, metrics) in Polyaxon.
Optimize hyperparameters using Polyaxon's built-in search
Optimal hyperparameters identified with automated search, tracked alongside all experiments.
Register and version models in Polyaxon Model Registry
Model is versioned, documented, and traceable to its training experiment in the registry.
Deploy model as a scalable API on Kubernetes
Model is live as a scalable, monitored API endpoint on Kubernetes.
Set up CI/CD pipeline for model retraining and redeployment
Model retraining and redeployment are fully automated with CI/CD, ensuring continuous improvement.
Deploy Polyaxon using Helm charts on an existing Kubernetes cluster. Configure persistent storage (e.g., NFS, S3) for experiment artifacts and model registry. Set up ingress and authentication (e.g., GitHub OAuth) for team access.
Why Huddle01 Cloud: Huddle01 Cloud provides managed Kubernetes clusters and GPU VMs, which are the core infrastructure needed to set up Polyaxon on Kubernetes.
Create a Polyaxon experiment using a YAML configuration file that specifies the Docker image, command, hyperparameters, and compute resources. Run the experiment via CLI or UI, and monitor logs/metrics in real-time.
Why Polyaxon: Polyaxon is the core tool for defining, running, and tracking ML experiments, directly matching the step's requirements.
Define a hyperparameter search space in the Polyaxon YAML file using 'matrix' or 'grid' sections. Run a group of experiments with automatic scheduling and early stopping to efficiently find optimal parameters.
Why Polyaxon: Polyaxon has built-in hyperparameter search and early stopping policies, directly fulfilling the step's needs.
After a successful experiment, promote the best model artifact to Polyaxon's Model Registry with metadata (metrics, tags, lineage). Use versioning to manage model iterations and rollback if needed.
Why Polyaxon: Polyaxon includes a Model Registry with experiment lineage metadata, directly supporting model versioning and registration.
Use Polyaxon's deployment feature to create a REST API endpoint for the registered model. Configure autoscaling, resource limits, and expose the service via Kubernetes ingress.
Why Polyaxon: Polyaxon supports model deployment on Kubernetes with autoscaling and monitoring, directly matching the deployment step.
Integrate Polyaxon with a CI/CD tool (e.g., GitHub Actions, GitLab CI) to automatically retrain models on new data, register new versions, and redeploy if performance improves.
Why GitLab: GitLab provides DevSecOps pipeline orchestration and automated code review, which can be used to set up CI/CD for model retraining and redeployment.
§ Before you start
Teams or solo builders working on data science & ml 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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