ZenML allows users to define ML workflows as simple Python functions decorated as steps, which are then composed into reproducible pipelines. This abstraction separates business logic from infrastructure details.
A 'stack' is a collection of infrastructure components (artifact store, orchestrator, etc.) that define where and how a pipeline runs. Users can easily switch between stacks (e.g., from local to AWS).
ZenML automatically tracks all inputs, outputs, and parameters for every pipeline run, storing them as versioned artifacts along with extensive metadata in a central repository.
The framework offers pre-built integrations with a wide array of popular MLOps tools, including experiment trackers (MLflow, Weights & Biases), orchestrators (Kubeflow, Airflow), and cloud services.
The commercial cloud platform provides a centralized web dashboard for visualizing pipelines, managing stacks, collaborating with team members, and monitoring runs across shared projects.
Data scientists use ZenML to structure their experimental Jupyter notebooks into formal pipelines. Each training run, with its specific data version, hyperparameters, and code, is automatically tracked. This allows scientists to precisely reproduce any past result, compare performance across hundreds of runs, and share definitive workflows with colleagues, eliminating the 'it worked on my machine' problem.
ML engineers leverage ZenML to create deployment pipelines that package a trained model, validate it against a test set, and deploy it to a serving platform like Seldon Core or KServe. By defining the deployment logic as a ZenML step, the process becomes repeatable and can be integrated into CI/CD systems, ensuring every model promotion follows the same rigorous, automated path to production.
Organizations with complex infrastructure use ZenML's stack concept to run identical training pipelines across different environments. A team can develop locally, test on a pre-production Kubernetes cluster, and run large-scale training on AWS Batch—all by switching the configured stack. This provides flexibility, avoids vendor lock-in, and optimizes costs by using the best infrastructure for each task.
Platform teams adopt ZenML as the foundational framework for their internal ML platform. They pre-configure approved stacks (e.g., with secure artifact stores and centralized experiment tracking) and provide them to data science teams. This empowers data scientists to self-serve while ensuring all projects adhere to company standards for security, reproducibility, and operational best practices.
Teams implement automated retraining pipelines using ZenML. The pipeline is triggered on a schedule or by an event (like data drift). It fetches new data, retrains the model, evaluates it against the current champion, and can automatically deploy the new model if it passes criteria. ZenML orchestrates this entire lifecycle, ensuring models in production stay accurate and up-to-date with minimal manual intervention.
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15Five operates in the people analytics and employee experience space, where platforms aggregate HR and feedback data to give organizations insight into their workforce. These tools typically support engagement surveys, performance or goal tracking, and dashboards that help leaders interpret trends. They are intended to augment HR and management decisions, not to replace professional judgment or context. For specific information about 15Five's metrics, integrations, and privacy safeguards, you should refer to the vendor resources published at https://www.15five.com.
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