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.
Product Overview
Generalized Linear Models (GLM) estimate regression models for outcomes following exponential distributions. In addition to the Gaussian (i.e. normal) distribution, these include Poisson, binomial, and gamma distributions. Each serves a different purpose, and depending on distribution and link function choice, can be used either for prediction or classification.
Key Data
Version
By
Type
Algorithm
Highlights
Generalized linear model, by H2O.ai from H2O-3 library
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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 PricingWith 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
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.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 | |
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.m4.4xlarge | $0.00 | |
ml.m4.10xlarge | $0.00 | |
ml.m4.16xlarge | $0.00 |
Usage Information
Fulfillment Methods
Amazon SageMaker
See http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/modeling.html#h2ogeneralizedlinearestimator for all hyperparameter definitions. NOTE: Required hyperparameter is "training", make sure to specify "family" for prediction as some distributions require categorical values. The data ingest process does not automatically encode categorical values
Metrics
Name | Regex |
|---|---|
MSE | MSE: ([0-9\.]*) |
RMSE | RMSE: ([0-9\.]*) |
LogLoss | LogLoss: ([0-9\.]*) |
AUC | AUC: ([0-9\.]*) |
auc_pr | auc_pr: ([0-9\.]*) |
AIC | AIC: ([0-9\.]*) |
Gini | Gini: ([0-9\.]*) |
Channel specification
Fields marked with * are required
training
*sdfsdfs
Input modes: File
Content types: csv
Compression types: None
Hyperparameters
Fields marked with * are required
training
*Training Parameters: family?, categorical_columns?, target?
Type: FreeText
Tunable: No
alpha
Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties.
Type: Continuous
Tunable: No
balance_classes
Balance training data class counts via over/under-sampling
Type: Categorical
Tunable: No
beta_epsilon
Converge if beta changes less (using L-infinity norm) than beta esilon, ONLY applies to IRLSM solver
Type: Continuous
Tunable: No
class_sampling_factors
Desired over/under-sampling ratios per class (in lexicographic order).
Type: FreeText
Tunable: No
compute_p_values
Request p-values computation, p-values work only with IRLSM solver and no regularization
Type: Categorical
Tunable: No
early_stopping
Stop early when there is no more relative improvement on train or validation
Type: Categorical
Tunable: No
fold_assignment
Cross-validation fold assignment scheme, if fold_column is not specified.
Type: FreeText
Tunable: No
fold_column
Column with cross-validation fold index assignment per observation.
Type: FreeText
Tunable: No
gradient_epsilon
Converge if objective changes less (using L-infinity norm) than this, ONLY applies to L-BFGS solver.
Type: Continuous
Tunable: No
ignore_const_cols
Ignore constant columns.
Type: Categorical
Tunable: No
ignored_columns
Names of columns to ignore for training
Type: FreeText
Tunable: No
interactions
A list of predictor column indices to interact. All pairwise combinations will be computed for the list.
Type: FreeText
Tunable: No
intercept
Include constant term in the model
Type: Categorical
Tunable: No
lambda_
Regularization strength
Type: Continuous
Tunable: No
lambda_min_ratio
Minimum lambda used in lambda search
Type: Continuous
Tunable: No
lambda_search
Use lambda search starting at lambda max, given lambda is then interpreted as lambda min
Type: Categorical
Tunable: No
link
One of: family_default, identity, logit, log, inverse, tweedie, ologit, oprobit, ologlog
Type: Categorical
Tunable: No
max_active_predictors
Maximum number of active predictors during computation.
Type: Integer
Tunable: No
max_after_balance_size
Maximum relative size of the training data after balancing class counts
Type: Continuous
Tunable: No
max_hit_ratio_k
Maximum number (top K) of predictions to use for hit ratio computation
Type: Integer
Tunable: No
max_iterations
Maximum number of iterations
Type: Integer
Tunable: No
max_runtime_secs
Maximum allowed runtime in seconds for model training. Use 0 to disable.
Type: Continuous
Tunable: No
missing_values_handling
Handling of missing values. Either MeanImputation or Skip.
Type: Categorical
Tunable: No
nfolds
Number of folds for K-fold cross-validation (0 to disable or >= 2).
Type: Integer
Tunable: No
nlambdas
Number of lambdas to be used in a search.
Type: Integer
Tunable: No
non_negative
Restrict coefficients (not intercept) to be non-negative
Type: Categorical
Tunable: No
obj_reg
Likelihood divider in objective value computation, default is 1/nobs
Type: Continuous
Tunable: No
objective_epsilon
Converge if objective value changes less than this
Type: Continuous
Tunable: No
offset_column
Offset column
Type: FreeText
Tunable: No
prior
Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of response does not reflect reality.
Type: Continuous
Tunable: No
remove_collinear_columns
In case of linearly dependent columns, remove some of the dependent columns
Type: Categorical
Tunable: No
solver
One of: auto, irlsm, l_bfgs, coordinate_descent_naive, coordinate_descent, gradient_descent_lh, gradient_descent_sqerr (default: auto).
Type: FreeText
Tunable: No
standardize
Standardize numeric columns to have zero mean and unit variance
Type: Categorical
Tunable: No
tweedie_link_power
tweedie link power
Type: Continuous
Tunable: No
tweedie_variance_power
tweedie variance power
Type: Continuous
Tunable: No
weights_column
Column with observation weights.
Type: FreeText
Tunable: No
Additional Resources
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Support Information
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