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.
Spotlight Implicit Factorization 0.9
By:
Latest Version:
0.9
A recommender system for implicit feedback datasets.
Product Overview
An implicit feedback matrix factorization model. Uses a classic matrix factorization approach, with latent vectors used to represent both users and items. Their dot product gives the predicted score for a user-item pair. The model is trained through negative sampling: for any known user-item pair, one or more items are randomly sampled to act as negatives (expressing a lack of preference by the user for the sampled item).
Key Data
Version
Categories
Type
Algorithm
Highlights
Please note: Models trained on GPU instances must use GPU instances for inference. Same applies to CPU-trained models.
Feature roadmap:
- allow exclude_items during inference (to exclude items already purchased/viewed by the user)
- support pipe mode
- support for supplying interaction weighting values
Now supports parameter tuning. Requires training and testing data channel for tuning.
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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.p3.2xlarge
Model Realtime Inference$0.00/hr
running on ml.p3.2xlarge
Model Batch Transform$0.00/hr
running on ml.p3.2xlarge
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$3.825/host/hr
running on ml.p3.2xlarge
SageMaker Realtime Inference$3.825/host/hr
running on ml.p3.2xlarge
SageMaker Batch Transform$3.825/host/hr
running on ml.p3.2xlarge
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.large | $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.xlarge | $0.00 | |
ml.m4.2xlarge | $0.00 | |
ml.m4.4xlarge | $0.00 | |
ml.m4.10xlarge | $0.00 | |
ml.m4.16xlarge | $0.00 | |
ml.c5.xlarge | $0.00 | |
ml.c5.2xlarge | $0.00 | |
ml.c5.4xlarge | $0.00 | |
ml.c5.9xlarge | $0.00 | |
ml.c5.18xlarge | $0.00 | |
ml.c4.xlarge | $0.00 | |
ml.c4.2xlarge | $0.00 | |
ml.c4.8xlarge | $0.00 | |
ml.c4.4xlarge | $0.00 | |
ml.p2.xlarge | $0.00 | |
ml.p2.8xlarge | $0.00 | |
ml.p2.16xlarge | $0.00 | |
ml.p3.2xlarge Vendor Recommended | $0.00 | |
ml.p3.8xlarge | $0.00 | |
ml.p3.16xlarge | $0.00 |
Usage Information
Fulfillment Methods
Amazon SageMaker
See example notebook for example usage.
Metrics
Name | Regex |
|---|---|
MRR | MRR: ([0-9\\.]+) |
Channel specification
Fields marked with * are required
training
*Training dataset. CSV file. Must include headers. Must include minimally columns titled 'user_id' and 'item_id'. Do not include any nulls or missing ids.
Input modes: File
Content types: text/csv
Compression types: None
testing
Optional testing dataset, but required for tuning. CSV file. Must include headers. Must include minimally columns titled 'user_id' and 'item_id'. Do not include any nulls or missing ids. If included, training logs will include MRR score.
Input modes: File
Content types: text/csv
Compression types: None
Hyperparameters
Fields marked with * are required
loss
Loss function
Type: Categorical
Tunable: Yes
embedding_dim
Number of embedding dimensions to use for users and items.
Type: Integer
Tunable: Yes
n_iter
Number of iterations to run
Type: Integer
Tunable: Yes
batch_size
Minibatch size
Type: Integer
Tunable: Yes
l2
L2 loss penalty
Type: Continuous
Tunable: Yes
learning_rate
Initial learning rate
Type: Continuous
Tunable: Yes
num_negative_samples
Number of negative samples to generate for adaptive hinge loss
Type: Integer
Tunable: Yes
random_seed
Random seed used to initialize random state to use when fitting
Type: Integer
Tunable: No
Additional Resources
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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.
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