Ashish Patel on LinkedIn: #data #jobs #artificialintelligence

Introducing Deepchecks - Tests for Continuous Validation of ML Models & Data $ pip install deepchecks -U --user Deepchecks is a Python package for comprehensively validating your machine-learning models and data with minimal effort. This includes checks related to various types of issues, such as model performance, data integrity, distribution mismatches, and more. While you're in the research phase and want to validate your data, find potential methodological problems, and/or validate your model and evaluate it. What Do You Need in Order to Start? Depending on your phase and what you wish to validate, you'll need a subset of the following: Raw data (before pre-processing such as OHE, string processing, etc.), with optional labels The model's training data with labels Test data (which the model isn't exposed to) with labels A supported model that you wish to validate, including: scikit-learn, XGBoost, PyTorch, and more.