ZenML combines forces with Great Expectations to add data validation to the list of continuous processes automated with MLOps. Discover why data validation is an important part of MLOps and try the new integration with a hands-on tutorial.
We built an end-to-end production-grade pipeline using ZenML for a customer churn model that can predict whether a customer will remain engaged with the company or not.
Connecting model training pipelines to deploying models in production is seen as a difficult milestone on the way to achieving MLOps maturity for an organization. ZenML rises to the challenge and introduces a novel approach to continuous model deployment that renders a smooth transition from experimentation to production.
With ZenML 0.6.3, you can now run your ZenML steps on Sagemaker, Vertex AI, and AzureML! It’s normal to have certain steps that require specific infrastructure (e.g. a GPU-enabled environment) on which to run model training, and Step Operators give you the power to switch out infrastructure for individual steps to support this.
Connecting model training pipelines to deploying models in production is regarded as a difficult milestone on the way to achieving Machine Learning operations maturity for an organization. ZenML rises to the challenge and introduces a novel approach to continuous model deployment that renders a smooth transition from experimentation to production.
Use MLflow Tracking to automatically ensure that you're capturing data, metadata and hyperparameters that contribute to how you are training your models. Use the UI interface to compare experiments, and let ZenML handle the boring setup details.
A dive into Python type hinting, how implementing them makes your codebase more robust, and some suggestions on how you might approach adding them into a large legacy codebase.
