Sold by: H2O.ai
Deployed on AWS
A multi-layer feedforward artificial neural network
Overview
H2O’s Deep Learning is based on a multi-layer feedforward artificial neural network that is trained with stochastic gradient descent using back-propagation. The network can contain a large number of hidden layers consisting of neurons with tanh, rectifier, and maxout activation functions. Advanced features such as adaptive learning rate, rate annealing, momentum training, dropout, L1 or L2 regularization, checkpointing, and grid search enable high predictive accuracy.
Highlights
- A feedforward artificial neural network (ANN) model, also known as deep neural network (DNN) or multi-layer perceptron (MLP), is the most common type of Deep Neural Network and the only type that is supported natively in H2O-3.
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Pricing
This product is available free of charge. Free subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimensions summary
This algorithm software carries no license fee, so you pay only the hourly infrastructure charge for the compute instance you run. Pricing splits into three activity types: training, batch inference, and real-time inference. Within each type, you choose from the c5, c4, m5, and m4 instance families in sizes ranging from xlarge up to 24xlarge. Larger instances offer more compute and bill at a higher hourly rate. You are charged per host hour (HostHrs) for the instance you select, matching the workload and instance size to your needs.
Top-of-mind questions for buyers
One HostHrs unit is one hour that a single compute instance runs your workload. You are billed for each instance you launch, per hour it stays active. A stopped instance stops accruing software host-hour charges. Underlying AWS infrastructure fees may still apply based on standard AWS terms.
Both meter per host hour on the instance you select. Batch inference runs predictions on stored datasets in scheduled jobs, so you pay only while the batch job runs. Real-time inference keeps an endpoint running to serve live requests, so charges accrue for the whole time the endpoint stays active.
Billing stays per host hour regardless of instance size. Larger instances in the c5, c4, m5, and m4 families bill at a higher hourly rate but on the same unit. You choose one instance per job, so your cost equals its hourly rate times the hours it runs.
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There is no refund policy as this algorithm is being offered for free
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Delivery details
Amazon SageMaker algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Initial Release of H2O-3 Deep Learning Algorithm
Additional details
Inputs
- Summary
See H2O-3 Deep Learning Algorithm Documentation for better usage recommendations. only required hyperparameter is "training" Recommend at least 5x amount of memory on machine as size of data.
- Input MIME type
- text/csv, csv, s3
See Input Summary
See Input Summary
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Vendor resources
Support
AWS infrastructure support
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