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The next wave of Artificial Intelligence is here and it is ruled by the transparency and explainability of AI-led decisions. It is now time for companies to take a leap from traditional ‘black-box’ ML modeling approach and adopt an explainable and scalable machine learning platform

ACT21 Software brings cutting-edge innovation to the banking and financial industry by providing reasoning and transparency into AI lead decisions. In partnership with we present to you an internationally acclaimed, award-winning Automatic Machine Learning (AutoML) platform – Driverless AI

Driverless AI

On average, 40% of companies take more than a month to deploy an ML model into production. H2O’s Driverless AI empowers data scientists to work on projects faster and more efficiently by using automation to accomplish key machine learning tasks in just minutes or hours, not months

Driverless AI
DAI Use Cases

Make Your Company an AI Company

DAI enables banks and financial institutions to provide customised, frictionless customer experiences, automate processed and drive customer profitability and loyalty

Using DAI you can

  • Deliver automatic feature engineering, model validation, model tuning, model selection and deployment, machine learning interpretability
  • Bring your own recipe, time-series and automatic pipeline generation for model scoring
  • It also provides companies with an extensible customizable data science platform that addresses the needs of a variety of use cases for banking and financial institutions

Win with DAI

Replace archaic methods and harness the power of cognitive technology with explainable artificial intelligence. DAI will give banks a significant edge in their digital transformation journey

Automatic Feature Engineering

Feature engineering is the secret weapon that advanced data scientists use to extract the most accurate results from algorithms. H2O’s Driverless AI employs a library of algorithms and feature transformations to automatically engineer new, high-value features for a given dataset

Automatic Feature Engineering
Bring your own recipe

Bring your own recipes

Data scientists can extend the Driverless AI platform by uploading their own models, transformers and scorers as a custom recipe. Bring-Your-Own recipes or use the examples built in the open and curated by the data science community. Driverless AI treats recipes as first-class citizens in the automatic machine learning workflow

Model Deployment And Operations

With H2O’s Driverless AI, models can be deployed automatically across a number of environment choices including creating a REST endpoint for any web applications to invoke the model, automatically run as a service in the cloud, or simply as a highly optimized Java code for edge devices.

Model Deployment
ML Cheatsheet

Machine Learning Interpretability

H2O Driverless AI provides robust interpretability of machine learning models to explain modeling results. In the MLI view H2O Driverless AI employs a host of different techniques and methodologies for interpreting and explaining the results of its models, four charts are generated automatically including: K-LIME, Shapley, Variable Importance, Decision Tree, Partial Dependence and more

Automatic Visualization

H2O’s Driverless AI automatically generates visualizations and creates data plots that are most relevant from a statistical perspective based on the most relevant data statistics to help users get a quick understanding of their data prior to starting the model building process

Atomic Visualization
Driverless AI NLP

Natural Language Processing

Text data can contain critical information to inform better predictions. Driverless AI automatically converts text strings into features using powerful techniques like TFIDF, CNN, and GRU. Driverless AI now also includes state-of-the-art PyTorch BERT transformers. With advanced NLP techniques, Driverless AI can also process larger text blocks and build models using all available data and to solve business problems like sentiment analysis, document classification, and content tagging

Image Processing

H2O’s Driverless AIdelivers state-of-the-art image processing capabilities using over 30 pre-trained image transformers and models including (SE)-ResNe(X)ts, DenseNets, MobileNets, EffientNets, and Inceptions. Images can be processed alone or as part of larger datasets that include tabular, text, and image data on CPUs or GPUs. Deliver computer vision and visual AI projects faster with automatic testing across all the leading techniques to find the best model with H2O Driverless AI

Image Processing
Time Series

Time Series

H2O Driverless AI delivers superior time-series capabilities to create forecasts. Driverless AI now includes SIERD for epidemic response so that customers can add COVID-19 models to their forecasts. With Driverless AI, users can forecast any prediction time window, incorporate data from numerous predictors, handle structured character data and high-cardinality categorical variables, and handle gaps in time series data and other missing values

Flexibility of Data Ingestion and Compute Technologies

H2O’s Driverless AIcan ingest data from a variety of data sets including Hadoop HDFS, Amazon S3, and more. H2O Driverless AI can also be deployed everywhere including all clouds (Microsoft Azure, AWS, Google Cloud) and on-premises on any systems.

H2O Driverless AI is optimized to work with the with the latest Nvidia GPUs, IBM Power 9 and Intel x86 CPUs and to take advantage of GPU acceleration to achieve up to 30X speedups for automatic machine learning. Driverless AI includes support for GPU accelerated algorithms like XGBoost, TensorFlow, LightGBM GLM, and more

Deployment Options and GPU Acceleration

Driverless AI Architecture

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