HuggingFace let the users build state-of-the-art models based on pre-trained transformers. With the Deepchecks-HuggingFace tutorial, you can learn how to validate such models.
Databricks is a managed data lake platform to handle the users' data, analytics, and use-cases. With the Deepchecks-Databricks integration, it is now possible to validate models built on the Databricks platform, even using Spark.
H2O.ai is an open-source machine learning and AutoML framework that makes it easy to build learning models. Deepchecks integrate with any H2O model and enable the validation and testing of them
The pytest framework is commonly used to write small, readable, and maintainable unit tests and is widely used. With the Deepchecks-pytest tutorial, you can learn how to write unit tests for your model that combines smart validation logic from the deepchecks package. These tests can later be integrated into your CI/CD pipeline.
Apache Airflow is an open-source workflow management system. It is commonly used to automate data processing, data science, and data engineering pipelines. It is commonly used as a CI/CD pipeline orchestrator for data and ML use cases. With the integration with Deepchecks, the checks and suites can be used to test and validate models built on Airflow and even cancel the model training/deployment if bad things happen.
Weights and Biases is an experiment tracking and model management platform. With the deepchecks-W&B integration, you can easily track the checks’ and suites’ results right in your weights and biases project, and to compare the results of different suites’ runs.