Machine Learning#
This tutorial section contains learning material on programmatically training, managing and deploying machine learning models in Dataiku.
Local Interpretable Model-agnostic Explanations#
This tutorial explains using LIME (Local Interpretable Model-agnostic Explanations) to provide human-readable explanations for machine learning model predictions.
Predictive maintenance#
This tutorial explains how can you predict performance before getting the ground truth.
Reinforcement learning#
This tutorial uses reinforcement learning (RL) to tune a random forest classifier’s hyperparameters automatically. The Q-learning algorithm explores and exploits hyperparameter combinations to find the best combination, using validation accuracy as the reward.
Experiment Tracking#
Pre-trained Models#
Model Import#
Model Export#
Distributed training#
Vulnerability and Bias Scanning with Protect AI Guardian#
This tutorial will guide you through the process of scanning a model for vulnerabilities, biases, and security concerns using Protect AI Guardian’s Python SDK.