Who should use the Automate MLOps workflows 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 automate mlops workflows with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
Model health is continuously monitored, and retraining is automated on drift or schedule.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
Model health is continuously monitored, and retraining is automated on drift or schedule.
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 Polyaxon to code and data are versioned, reproducible, and stored remotely. Then, you pass the output to Lindy AI to model training and evaluation run automatically on code/data changes with tracked metrics. Then, you pass the output to Make to automated validation gates prevent poor-quality models from progressing. Then, you pass the output to Polyaxon to validated models are automatically deployed to a production serving environment. Finally, Glean AI is used to model health is continuously monitored, and retraining is automated on drift or schedule.
Set up version-controlled ML code and data pipeline
Code and data are versioned, reproducible, and stored remotely.
Automate model training and evaluation pipeline
Model training and evaluation run automatically on code/data changes with tracked metrics.
Implement automated model validation and testing
Automated validation gates prevent poor-quality models from progressing.
Automate model deployment and serving
Validated models are automatically deployed to a production serving environment.
Set up monitoring and automated retraining
Model health is continuously monitored, and retraining is automated on drift or schedule.
Initialize a Git repository for your ML code and use DVC (Data Version Control) to track datasets and model artifacts. Configure a remote storage (e.g., S3, GCS) for DVC cache. This ensures reproducibility and collaboration from the start.
Why Polyaxon: Polyaxon supports experiment tracking and model deployment, which aligns with version-controlled ML pipelines, though no tool perfectly matches Git/DVC/S3 needs.
Use a CI/CD platform (e.g., GitHub Actions, GitLab CI) to trigger training on code or data changes. Write a pipeline that pulls the latest data, runs training, evaluates metrics, and logs results to an experiment tracker (e.g., MLflow).
Why Lindy AI: Polyaxon offers experiment tracking and hyperparameter optimization, closest to MLflow/GitHub Actions needs for training automation.
Add a validation stage in the pipeline that runs unit tests on data quality, model performance thresholds, and fairness checks. Use tools like Great Expectations for data validation and pytest for code tests. Fail the pipeline if thresholds are not met.
Why Make: Make provides AI-agent workflow orchestration and automated reporting, which can support validation pipeline automation.
Set up a deployment pipeline that packages the validated model (e.g., as a Docker container) and deploys it to a serving infrastructure (e.g., Kubernetes, SageMaker, or a REST API). Use a model registry (e.g., MLflow Model Registry) to manage versions and promote models to staging/production.
Why Polyaxon: Polyaxon directly supports model deployment, which is the core need for this step.
Implement monitoring for model performance drift (e.g., using Evidently AI or Prometheus) and data drift. Configure alerts and trigger a retraining pipeline when drift exceeds thresholds. Use a scheduler (e.g., cron, Airflow) for periodic retraining if needed.
Why Glean AI: Glean AI can automate data analysis and report generation, partially covering monitoring needs.
§ 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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