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Amazon Sagemaker

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

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H2O.ai H2O-3 Automl Algorithm

By:
H2O.ai
Latest Version:
0.2
1 AWS review
Implementation of H2O.ai 's AutoML Algorithm from H2O-3 Library
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    Product Overview

    H2O’s AutoML can be used for automating the machine learning workflow, which includes automatic training and tuning of many models within a user-specified time-limit. Stacked Ensembles – one based on all previously trained models, another one on the best model of each family – will be automatically trained on collections of individual models to produce highly predictive ensemble models which, in most cases, will be the top performing models in the AutoML Leaderboard.

    Key Data

    Version
    0.2
    Usage Instructions
    Release Notes
    By
    H2O.ai
    Categories
    ML Solutions
    Classification-Structured
    Regression
    Type
    Algorithm
    Fulfillment Methods
    Amazon SageMaker

    Highlights

    • In order for machine learning software to truly be accessible to non-experts, we have designed an easy-to-use interface which automates the process of training a large selection of candidate models. H2O’s AutoML can also be a helpful tool for the advanced user, by providing a simple wrapper function that performs a large number of modeling-related tasks that would typically require many lines of code, and by freeing up their time to focus on other aspects of the data science pipeline tasks such as data-preprocessing, feature engineering and model deployment.

    Not quite sure what you’re looking for? AWS Marketplace can help you find the right solution for your use case. Contact us

    Pricing Information

    Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.


    Estimating your costs

    Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.

    Version
    Region

    Software Pricing

    Algorithm Training$0.00/hr

    running on ml.c5.2xlarge

    Model Realtime Inference$0.00/hr

    running on ml.c5.2xlarge

    Model Batch Transform$0.00/hr

    running on ml.c5.2xlarge

    Infrastructure Pricing

    With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
    Learn more about SageMaker pricing

    SageMaker Algorithm Training$0.408/host/hr

    running on ml.c5.2xlarge

    SageMaker Realtime Inference$0.408/host/hr

    running on ml.c5.2xlarge

    SageMaker Batch Transform$0.408/host/hr

    running on ml.c5.2xlarge

    Algorithm Training

    For algorithm training in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.
    InstanceType
    Algorithm/hr
    ml.m5.xlarge
    $0.00
    ml.m5.2xlarge
    $0.00
    ml.m5.4xlarge
    $0.00
    ml.m5.12xlarge
    $0.00
    ml.m5.24xlarge
    $0.00
    ml.m4.2xlarge
    $0.00
    ml.m4.10xlarge
    $0.00
    ml.m4.4xlarge
    $0.00
    ml.m4.16xlarge
    $0.00
    ml.c5.2xlarge
    Vendor Recommended
    $0.00
    ml.c5.4xlarge
    $0.00
    ml.c5.9xlarge
    $0.00
    ml.c5.18xlarge
    $0.00
    ml.c4.2xlarge
    $0.00
    ml.c4.4xlarge
    $0.00
    ml.c4.8xlarge
    $0.00

    Usage Information

    Fulfillment Methods

    Amazon SageMaker

    Can be used for classification or regression problems. Required input: 1 training dataset, hyperparameters defining the type of problem being solved and the target column in the training dataset. Default is classification.

    Metrics

    Name
    Regex
    logging
    (.*)

    Channel specification

    Fields marked with * are required

    training

    *
    training data
    Input modes: File
    Content types: csv
    Compression types: None

    Hyperparameters

    Fields marked with * are required

    training

    *
    Training Parameters: classification?, categorical_columns?, target?
    Type: FreeText
    Tunable: No

    max_models

    Maximum number of models to build
    Type: Integer
    Tunable: No

    max_runtime_secs

    Controls how long the AutoML run will execute
    Type: Integer
    Tunable: No

    stopping_metric

    stops training new models in the AutoML run when the option selected for stopping_metric doesn’t improve for the specified number of models
    Type: FreeText
    Tunable: No

    stopping_rounds

    Specifies the metric to use for early stopping.
    Type: Integer
    Tunable: No

    seed

    seed for reproducibility
    Type: Integer
    Tunable: No

    exclude_algos

    List of algorithms to exclude/skip
    Type: FreeText
    Tunable: No

    stopping_tolerance

    specifies the relative tolerance for the metric-based stopping to stop the AutoML run
    Type: Continuous
    Tunable: No

    max_after_balance_size

    Maximum relative size of the training data after balancing class counts
    Type: Integer
    Tunable: No

    nfolds

    Number of folds for k-folds cross validation
    Type: Integer
    Tunable: No

    balance_classes

    Balance training data class counts
    Type: Categorical
    Tunable: No

    Additional Resources

    End User License Agreement

    By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)

    Support Information

    AWS Infrastructure

    AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.

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    Refund Policy

    No refund policy is available, since the offering is free to use

    Customer Reviews

    HS
    Runs over 2 years old H2O version, debugging is a nightmare.
    Mar 3, 2021Verified purchase review from AWS Marketplace
    This version of h2o.automl is over 2 years old compared to the latest h2o automl available outside
    AWS. This gives rise to cryptic errors when using a moderate to large size number of models that are
    hard to debug and with limited support. Product did not work for my ML application, had t... Read more    ... Read more
    View all

    Reviews from AWS Marketplace

    1 AWS review