Overview of H2O Driverless AI
H2O Driverless AI is an advanced artificial intelligence platform designed to automate and streamline the most complex data science and machine learning workflows. This platform is tailored to empower data scientists and organizations to develop and deploy highly accurate predictive models with unprecedented speed and efficiency.
Key Functionality
- Automated Machine Learning Workflows: Driverless AI automates critical tasks such as data visualization, feature engineering, model validation, model tuning, model selection, and model deployment. This end-to-end automation enables users to achieve predictive accuracy comparable to that of expert data scientists but in a significantly shorter timeframe.
Core Features
- Feature Engineering: Driverless AI automatically detects relevant features in a dataset, handles missing values, derives new features, and evaluates the importance of each feature. It transforms raw data into meaningful values that machine learning algorithms can efficiently consume.
- Model Development and Deployment: The platform reduces the time required to develop accurate, production-ready models by automating tasks like model selection, hyperparameter tuning, and model stacking. It creates easy-to-deploy, low-latency scoring pipelines, leveraging high-performance computing with both CPUs and GPUs to compare thousands of combinations and iterations quickly.
- Machine Learning Interpretability (MLI): Driverless AI provides automatic visualizations and machine learning interpretability, which are crucial in regulated industries where model transparency and explanation are as important as predictive performance. The platform includes a module for global and local model interpretation.
- Support for Various Data Types: The platform supports tabular structured data, including numeric, categorical, and textual fields. It also handles images, time-series data (both single and grouped), and allows for custom recipes to process video, audio, and graph data.
- Advanced NLP and Image Processing: Driverless AI includes powerful natural language processing (NLP) techniques such as TFIDF, CNN, GRU, and state-of-the-art PyTorch BERT transformers to convert text into features. It also supports advanced image processing for tasks like sentiment analysis, document classification, and content tagging.
- High-Performance Computing: The platform is optimized for multi-GPU and multi-CPU environments, including NVIDIA DGX-1 supercomputers, to accelerate training and model development significantly.
- User-Friendly Interface and APIs: Driverless AI offers a user-friendly GUI along with Python and R client APIs, making it accessible to users of various backgrounds. It also supports multi-user environments and backward compatibility.
- Customization and Extensibility: Users can customize and extend the platform using over 130 open-source recipes or their own domain expertise. This flexibility allows the platform to address a wide range of use cases across different industries.
Deployment and Integration
- Data Sources and File Formats: Driverless AI supports data from various sources including local file systems, cloud storage (S3, Azure Blob, Google Cloud), Hadoop (HDFS), and databases (JDBC). It can read multiple file formats such as CSV, Excel, Parquet, and more.
- Scoring Pipelines: The platform generates standalone batch scoring pipelines and low-latency scoring artifacts in Python, Java, and C (with R and Python runtimes), enabling easy deployment via HTTP or TCP protocols.
In summary, H2O Driverless AI is a robust AI platform that leverages automation and high-performance computing to accelerate machine learning workflows, ensuring high predictive accuracy, model transparency, and ease of deployment. It is designed to empower data scientists and organizations to derive maximum value from their data efficiently.