Deepchecks: Enabling automated testing of your ML models.

6 min readJun 26, 2023

Introduction

Deepchecks is a groundbreaking open-source Python package that aims to simplify and enhance the process of implementing automated testing for machine learning (ML) models. It goes beyond traditional testing frameworks by providing up-to-date ML validation best practices and a set of default tests that can be easily integrated into existing ML pipelines.

With Deepchecks, developers can start incorporating automated testing early in their workflow and gradually build up their test suites as they go. In this article, we will explore the various aspects of Deepchecks and how it can revolutionize the way we validate and maintain ML models.

Why Deepchecks?

Deepchecks offers several compelling features that set it apart from other testing frameworks and make it an attractive choice for ML practitioners:

Comprehensive ML Testing:

Deepchecks provides a wide range of checks and validations for ML models and data. These include tests for model performance, data integrity, distribution mismatches, and more. By covering a broad spectrum of issues, Deepchecks ensures that ML models are thoroughly evaluated and validated.

Readable Test Results:

One of the standout features of Deepchecks is its ability to present test results in a human-readable format. The package includes carefully crafted interpretations that make it easy for both ML experts and beginners to understand what went wrong and how to fix it. This makes the debugging process much more efficient and helps teams identify and resolve issues quickly.

Fresh Perspective on ML Testing:

Unlike many other tools that try to apply traditional software development practices to the ML domain, Deepchecks takes a methodical and fresh approach to ML testing. It acknowledges the fundamental differences between ML and traditional software development and redefines the testing process accordingly. This approach ensures that ML models are evaluated in a way that is tailored to their unique characteristics.

Easy Integration with Existing Pipelines:

Deepchecks is designed to seamlessly integrate into existing ML pipelines. Developers can easily incorporate Deepchecks tests into their workflows and configure them to suit their specific needs. This flexibility allows teams to adopt Deepchecks without disrupting their existing processes and workflows.

Your reaction when it does all the work for you 😃

End-to-End Machine Learning pipeline

write one sentences for each instead of two

machine learning pipeline steps ingestion preprocessing training evaluation deployment production

ANSWER | PHIND MODEL

Ingestion: In the ingestion step, data is collected from various sources, such as databases or APIs, and prepared for use in the machine learning pipeline. This process may involve cleaning, organizing, and transforming the data to ensure it is in a suitable format for subsequent steps.
Preprocessing: During preprocessing, the collected data is transformed and prepared for training the machine learning model. This step includes tasks such as data cleaning, feature extraction, feature engineering, data normalization, and handling missing values to ensure the data is in a suitable format and representation for the training and evaluation steps.
Training: In the training step, the machine learning model is trained on the preprocessed data. The model learns from the input data and adjusts its internal parameters to make predictions or classifications based on the provided training examples. The training process involves selecting an appropriate algorithm, defining the model architecture, and optimizing the model’s performance using techniques such as gradient descent or backpropagation.
Evaluation: The evaluation step assesses the performance of the trained machine learning model using separate datasets, typically referred to as validation or test sets. Evaluation metrics, such as accuracy, precision, recall, F1 score, and area under the curve (AUC), help determine if the model meets the desired performance criteria or if further adjustments are needed.
Deployment: Once the model has been evaluated and deemed satisfactory, it is deployed to a production environment where it can make predictions or classifications on new, unseen data. Deployment involves integrating the model into an application or system that can utilize its predictions in real-time, such as creating APIs, web services, or other interfaces to interact with the model and feed it with new data for inference.
Production: In the production phase, the deployed model is actively used to make predictions or classifications on a continuous basis. Monitoring and maintaining the model’s performance in a production environment is essential to ensure its accuracy and reliability over time. This may involve monitoring data drift, retraining the model periodically, and updating the model as new data becomes available or business requirements change.

The Deepcheck suits are integrated within these machine-learning pipeline to automate the flow and do validation and integrity testing, train-test validation testing and model evaluation based on checks.

Application of Deepchecks in inter-disciplinary fields:

Data Integrity Suits:

Data Suits (collection of checks) validates the integrity and quality of data. The set of validation checks such as, check for missing values, outliers, duplicates, and data consistencies.

Percentage of Nulls on real world dataset.

Some Common checks:

Data IntegrityMixed Nulls, Percent Of Nulls, String Mismatch, String Length Out Of Bounds, Feature Label Correlation, Is Single Value, Feature Feature Correlation, Columns Info, Class Imbalance, Outlier Sample Detection, Identifier Label Correlation, Conflicting Labels, Data Duplicates, Special Characters, Mixed Data Types.

Train Test Validation:

Ensures that the splits are done properly and that the data is representative across the splits. Verify the distribution consistency among splits.

Some common checks:

Train Test Split Usefulness:

Provides an unbiased evaluation of machine learning models by partitioning data into separate train and test sets.
Identify potential data leakage issues between the train and test datasets
Allows for the comparison of model performance on the train and test datasets, aiding in detecting overfitting or underfitting.
Highlight feature drift and multivariate drift between the train and test datasets.

Model Evaluation Suits:

Thorough analysis of the model’s performance before deploying it.
Evaluation of a proposed model during the model selection and optimization stage.
Checking the model’s performance on a new batch of data.

A single check for model evaluation based on real world data.

Some common checks:

Model Evaluation Evaluation:

Evaluate your model’s performance before deployment, during model selection and optimization, or on new batches of data.
Provides easy interpretation of flaws in data and machine learning models for real-world implementation.
Generates interpretable and useful reports that help analyze discrepancies in data
Ensure the reliability and dependable outcomes of machine learning models tested and validated on real-time data or changing parameters.