H2O Driverless AI - Detailed Review

H2O Driverless AI Overview

H2O Driverless AI is an advanced automated machine learning (AutoML) platform that simplifies and accelerates the process of building, deploying, and interpreting machine learning models. Here are the key features and functionalities of H2O Driverless AI:

Automated Feature Engineering

H2O Driverless AI automates the entire feature engineering process, which is crucial for extracting valuable insights from data. It detects relevant features, finds interactions between them, handles missing values, and derives new features from the existing data. The platform compares existing and newly generated features and shows their relative importance, transforming them into meaningful values that machine learning algorithms can easily consume.

Automated Model Development

Driverless AI reduces the time required to develop accurate, production-ready models by automating tasks such as model selection, hyperparameter tuning, and model stacking. It uses a genetic algorithm to train multiple models, incorporate hyperparameter tuning, scoring, and ensembling, and determines the best model based on the configured options. This process can be completed in minutes or hours, significantly faster than manual approaches.

Comprehensive Explainability Toolkit

The platform provides robust interpretability of machine learning models through its Machine Learning Interpretability (MLI) and fairness dashboards. It offers automated model documentation, reason codes for each model prediction, and global and local explanations for model outputs. This ensures transparency and trust across the entire machine learning lifecycle, which is particularly important in regulated industries.

Expert Recommender System

H2O Driverless AI includes an AI Wizard that investigates the data, provides recommendations based on business requirements, and suggests appropriate machine learning techniques. This system is based on data science best practices from various disciplines, ensuring that the customized model aligns with the user’s business needs.

Data Visualization and Quality

The platform automatically visualizes data and addresses data quality issues. It generates visualizations based on the most relevant data statistics, helping users quickly understand their data and identify potential issues such as missing values or outliers.

Deployment Flexibility

Driverless AI allows models to be deployed across various environments, including creating REST endpoints for web applications, running as a service in the cloud, or deploying as highly optimized Java code for edge devices. It supports deployment on multiple clouds (AWS, Azure, Google Cloud) and on-premises systems, with a particular advantage when run on systems with GPUs, such as IBM Power 9 and NVIDIA DGX-1.

GPU Acceleration

The platform is optimized to take advantage of GPU acceleration, achieving significant speedups (up to 40X) for automated machine learning tasks. It includes multi-GPU algorithms for various machine learning techniques like XGBoost, GLM, and K-Means.

Automatic Model Documentation

Driverless AI provides an Autoreport (Autodoc) for each experiment, which includes details about the data used, validation schema, model and feature tuning, and the final model created. This automated documentation saves time and ensures that models are well-documented and explainable.

Operational Efficiency

The platform streamlines the data science process by automating many of the time-consuming tasks, allowing data scientists and analysts to focus more on drawing actionable insights from the models rather than on the development, validation, and deployment process. It also ensures that models are moved efficiently from development to production environments.

Conclusion

By integrating these features, H2O Driverless AI makes it possible for users of all backgrounds to leverage the power of machine learning without the need for extensive coding or deep data science expertise.