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

• With XGBoost and custom pre-processing

• With Keras/Tensorflow for sentiment analysis

• With Catboost

• With LightGBM

• With scikit-learn

Pre-trained Models

• Tensorflow

• PyTorch

• Hugging Face

• SentenceTransformers

• spaCy

• NLTK

Model Import

• Importing serialized scikit-learn pipelines as Saved Models for MLOps

Model Export

• Wrapping an exported model in a CLI tool

• Deploying a model to an edge device with AWS IoT Greengrass

Distributed training

• Distributed ML Training using Ray

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.