Delivering AI at Scale 

Businesses are creating and driving an AI strategy to gain a  competitive edge. There are three critical challenges to  achieving AI at scale, including addressing the talent gap,  the time a model takes to train, tune and deploy, and  being able to trust the AI. H2O Driverless AI is an award-winning and industry-leading automatic machine  learning platform that empowers data science teams to  scale and deliver trusted, production-ready models to  fulfill every business AI strategy and initiatives.

 

Filling the Talent Gap 

Data scientists are in short supply for all but the largest technology  companies.  With  Driverless  AI,  expert  and  novice data scientists, data engineers, domain scientists, mathematicians, and statisticians in all businesses can develop highly accurate models that are ready to deploy.  H2O  Driverless  AI  was  created  and  developed by expert data scientists so that they can use the platform to perform the tasks of an advanced data  scientist  automatically. This  enables  your  data  scientist to focus on evaluating results and exploring new use cases.

 

More Models in Less Time 

Reducing the time that it takes to develop accurate,  production-ready models is critical to solving a large number of business challenges with AI. Driverless AI  automates time-consuming data science tasks including,  advanced feature engineering, model selection,  hyperparameter tuning, model stacking, and model  deployment. These processes can be accelerated with  high-performance computing on GPU and CPU systems  that allow for thousands of combinations and iterations to  be tested and to find the best model in hours, not  months. Model deployment also is streamlined with  automatic scoring pipelines that include everything that is needed to run the model in production.

 

Trusted AI Results 

To adopt AI models at scale, business teams and  regulators must be able to interpret and trust AI results.  H2O Driverless AI delivers highly accurate models, but  also provides vital capabilities for understanding,  debugging and sharing model results including Machine  Learning Interpretability (MLI) dashboards, automated  model documentation and reason codes for service  representatives and customers.

Key Capabilities of H2O Driverless AI

AutoVis – Exploratory Data Analysis 

H2O Driverless AI automatically selects data plots based on  the most relevant data statistics to help users understand  their data prior to the model building process. This is useful  for understanding the composition of very large data sets  and discovering trends and possible issues such as large  numbers of missing values or significant outliers that could  impact modeling results. It also provides recommendations  for transformations to address the problems identified.

 

 

Automatic Feature Engineering and Model Building 

Feature engineering is the secret weapon that advanced  data scientists use to extract the most accurate results  from algorithms. H2O Driverless AI employs a library of  algorithms and feature transformations to automatically  engineer new, high-value features for a given data set.  Included in the interface is an easy to read variable  importance chart that shows the significance of original  and newly engineered features.

 

Machine Learning Interpretability (MLI) 

Driverless AI provides robust explainability for machine  learning models including, K-LIME, LIME-SUP, Shapley,  Variable Importance, Decision Tree Surrogate, ICE, and  Partial Dependence Plots. Each of these techniques helps to  explore and demystify modeling results. Driverless AI now  also includes straightforward disparate impact analysis to  test for algorithmic discrimination. Maximum transparency  and minimal disparate impact are crucial differentiators for  those who must explain and justify their models to business  stakeholders and regulators.

 

 

Automatic Reason Codes 

For many regulated industries, an explanation is required  for significant decisions relating to customers, e.g., credit  denial. Reason codes show the key positive and negative  factors in a model’s scoring decision in simple language.

Reason codes are also useful in other settings, such as  healthcare, because they can provide insights into model  decisions that can drive additional testing or investigation.

 

Automatic Model Documentation (Auto Doc) 

To explain models to business users and regulators, data  scientists must document the data, algorithms, and processes  used to create machine learning models. Driverless AI  automatic model documentation relieves the user from the  time-consuming task of documenting and summarizing their  workflow while building machine learning models. The  documentation includes details about the data used, the  validation schema selected, model and feature tuning, MLI, and the final model created. Auto Doc saves data scientists  time, which can then be used to train and deploy more  models.

 

Automatic Scoring Pipelines 

H2O Driverless AI automatically generates both Python scoring and Java ultra-low latency automatic scoring pipelines. The automatic scoring pipeline is a unique technology that deploys the feature engineering and the winning machine learning model or ensemble in a highly optimized format that can be deployed anywhere. This technology is critical for enterprises running models that need ultra-fast scoring for real-time applications running on a range of devices.

 

Bring-Your-Own Recipes to Make Your Own AI 

H2O Driverless AI can be extended by data scientists to help  them make their own AI, with customizations – models, transformers, and scorers – on the platform. These customizations, called recipes, are then treated as first-class citizens in the automatic machine learning optimization process and eventually creating the winning model. Data science teams can explore and consume custom recipes from the library to improve models. They can also develop customizations specific to their use-cases, industry, or their enterprise.

 

Time Series Recipes 

Time-series forecasting is one of the biggest challenges for  data scientists. Time-series models address key use-cases  including demand forecasting, infrastructure monitoring, and  predictive maintenance based on the transaction, log, and  sensor data. H2O Driverless AI delivers superior time series  capabilities to optimize for almost 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. Driverless AI also provides MLI for  time-series models.

 

Enterprise-Ready 

H2O Driverless AI is scalable, secure, and connects to a variety  of data sources whether in the cloud or on-prem. H2O.ai are  the experts in data science at scale. Driverless AI customers  enjoy a full range of support, training, and expertise to assist  them with their AI journey.

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