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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Plug and Predict
By:
Latest Version:
v1.2.1
Platform for Automated Feature Engineering, Discovery and machine learning modeling at scale.
Product Overview
Plug and Predict enables you to get the best features and model for your prediction problem, automatically! Leveraging evolutionary algorithms and ensemble models, Plug and Predict sifts through the high-dimensional search space of features and models to figure out the best possible solution for your prediction problem. The only inputs needed are transactional data and the specifications for your prediction problem. Get the right features and model for any binary classification prediction problem involving transactional data.
Key Data
Version
By
Categories
Type
Algorithm
Highlights
AI-powered feature engineering, discovery and modelling for prediction problems.
Easily integrated into your existing workflow via API call.
A sample dataset has been included in this to enable you to try Plug n Predict for free.
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.
Contact us to request contract pricing for this product.
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
Annual$800,000.00/yr
running on any instance
Algorithm Training$350/hr
running on ml.m5.12xlarge
Model Realtime Inference$0.00/hr
running on ml.m5.large
Model Batch Transform$0.00/hr
running on ml.m5.large
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$2.765/host/hr
running on ml.m5.12xlarge
SageMaker Realtime Inference$0.115/host/hr
running on ml.m5.large
SageMaker Batch Transform$0.115/host/hr
running on ml.m5.large
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.m5.4xlarge | $350.00 | |
ml.m5.12xlarge Vendor Recommended | $350.00 | |
ml.m5.2xlarge | $350.00 |
Usage Information
Fulfillment Methods
Amazon SageMaker
Input and Output Details Please refer to Plug and Predict user guide for more details. Please reach out to ZS team engaged with client to get the latest user guide. Hyper tuning Parameters Please refer to Plug and Predict user guide for more details. Please reach out to ZS team engaged with client to get the latest user guide.
Below are the recommended Instance types, number of instances and storage information for successful execution of algorithm.
Auto Feature Transformer | Dataset | Generations | Instance type | # of Instances | storage/instance | |------------|---------------|--------------------|------------------|---------------------| | <= 10 GB | 50 | ml.m5.2xlarge | 10 | 40 GB | | <= 10 GB | 100 | ml.m5.4xlarge | 5 | 40 GB | | <= 10 GB | 200 | ml.m5.4xlarge | 10 | 40 GB | | 10-20 GB | 50 | ml.m5.12xlarge | 10 | 80 GB | | 10-20 GB | 100 | ml.m5.12xlarge | 10 | 80 GB | | 10-20 GB | 200 | ml.m5.12xlarge | 10 | 80 GB |
Auto Feature Transformer | Dataset | Generations | Instance type | # of Instances | storage/instance | |------------|---------------|--------------------|------------------|---------------------| | <= 10 GB | 200 | ml.m5.4xlarge | 10 | 40 GB | | 10-20 GB | 200 | ml.m5.12xlarge | 10 | 80 GB |
Rule Parser | Dataset | Instance Type | # of Instances | Storage/instance | |------------|------------------|-----------------|---------------------| | <= 10 GB | ml.m5.4xlarge | 5 | 40 GB |
Channel specification
Fields marked with * are required
train
Input modes: File
Content types: -
Compression types: -
test
Input modes: File
Content types: -
Compression types: -
attribute_config
Input modes: File
Content types: -
Compression types: -
inference_model
Input modes: File
Content types: -
Compression types: -
event_code_mapping
Input modes: File
Content types: -
Compression types: -
Hyperparameters
Fields marked with * are required
generations
Number of generations
Type: Integer
Tunable: No
population_size
Population size in each generation
Type: Integer
Tunable: No
tournament_size
Best candidates to move into next generation
Type: Integer
Tunable: No
hgs_granularity
Time search granularity
Type: Integer
Tunable: No
reach_percentage_cutoff
Minimum threshold for categorical variables
Type: Continuous
Tunable: No
fitness_cutoff
Fitness threshold
Type: Continuous
Tunable: No
max_days
Past days to consider
Type: Integer
Tunable: No
stopping_criteria
Early stopping criteria threshold value
Type: Continuous
Tunable: No
candidate_singularity_flag
Perform candidate singularity
Type: Integer
Tunable: No
is_transform
Set to true if only inference required
Type: Categorical
Tunable: No
module
Set to true if only inference required
Type: Categorical
Tunable: No
is_debug
Set to true if debug logs are required
Type: Categorical
Tunable: No
Additional Resources
End User License Agreement
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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.
Learn MoreRefund Policy
Refund policy is as per End User License Agreement.
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