Video by H2O.ai via YouTube
How H2O.ai bridges visual no-code ML pipelines and code-first Python execution for diverse data science working styles.
AI teams contain visual thinkers, coders, and everyone in between. Driverless AI supports intuitive wizards and visual pipeline diagrams for feature engineering and model tuning. MLOps allows switching between UI-based row scoring and command-line batch execution. h2oGPTe agents generate sandbox-tested Python code that can be exported, modified, and used in automated testing—enabling teams to fluidly transition from a visual interface to a fully scriptable environment without losing any work.
Technical Capabilities & Resources
➤ Visual Pipeline Composition: Visualize feature engineering, model selection, and ensembling steps as interactive diagrams in Driverless AI.
🔗 https://docs.h2oai.com/driverless-ai/latest-stable/docs/userguide/scoring_pipeline_visualize.html
➤ No-Code to Code Conversion: Export UI workflows from Driverless AI into reproducible, executable Python scripts.
🔗 https://docs.h2o.ai/driverless-ai/latest-stable/docs/userguide/examples/autoviz_client_example/autoviz_client_example.html
➤ Custom Code Integration: Incorporate custom functions and recipes directly into Driverless AI workflows for granular control.
🔗 https://docs.h2o.ai/driverless-ai/latest-stable/docs/userguide/custom_recipes.html