Sold by: nannyML
Deployed on AWS
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Measure the impact of concept shift on your model's performance.
Overview
Concept shift occurs when the assumptions upon which a model was built no longer hold true due to changes in the data distribution or the nature of the problem itself.
Detecting it is a complex problem since it often requires ground truth data to do so.
At nannyML, we pose the following question to measure the impact of Concept Shift on model performance:
What would the performance of my model be on a reference dataset if the world operates as described by the latest available data?
The algorithm consists of the following steps:
-
Learn the latest concept from the model's features and targets.
-
Make predictions on reference data using the learned concept.
-
Estimate the model's performance assuming the previously learned concepts are ground truth.
If we consider the latest data's concept as truth, this algorithms enables us to understand the impact that a concept shift would have had on the model's performance.
Highlights
- Measure the impact of Concept Drift on your model's performance.
- Validate if your performance changes are due to Concept Shift.
- Get access to nannyML's most powerful algorithm yet.
Details
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Free trial
Try this product free for 7 days according to the free trial terms set by the vendor.
Pricing is based on actual usage, with charges varying according to how much you consume. 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.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $14.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $14.00 |
ml.m5.large Training Recommended | Algorithm training on the ml.m5.large instance type | $9.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $14.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $14.00 |
ml.m5.12xlarge Inference (Batch) | Model inference on the ml.m5.12xlarge instance type, batch mode | $14.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $14.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $14.00 |
ml.c4.4xlarge Inference (Batch) | Model inference on the ml.c4.4xlarge instance type, batch mode | $14.00 |
ml.m5.xlarge Inference (Batch) | Model inference on the ml.m5.xlarge instance type, batch mode | $14.00 |
Dimensions summary
You pay by the host hour, based on the SageMaker instance type you run. Pricing splits into three activity types: batch inference, real-time inference, and training. Within each activity, you choose from m4, m5, c4, and c5 instance families in sizes from large up to 24xlarge. Larger instances carry higher hourly rates, so cost scales with the compute you select. There are no tiers or commitments here. You only pay for the hours each instance runs, plus standard AWS infrastructure charges.
Top-of-mind questions for buyers
One HostHr is one hour that a single SageMaker instance runs the software. Billing meters wall-clock running time on the instance type you pick. Each running instance accrues its own hourly charge. If you run several instances at once, each one bills separately for its own hours.
Each activity bills independently by the hours its instances run. Training charges apply while you train a model. Inference charges apply while an endpoint runs, in either batch or real-time mode. You can incur all three at once, and each appears as its own line by instance type.
Charges accrue only while an instance actively runs. Batch inference bills for the hours the batch job runs, then stops. Real-time inference bills for as long as the endpoint stays up. Training bills only during the training run. Underlying AWS storage or other resources may still apply separately.
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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
Release of Concept Shift Algorithm!
Additional details
Inputs
- Summary
The input should be a CSV file. It should contain the names of the columns in the first row.
The required columns depend on the "parameters" defined during training. For more information read our documentation notebook .
The required number of rows depend on the chunking method defined during training.
- Limitations for input type
- For now, we only support binary classification problems, so the "problem_type" hyperparameter should be "classification_binary" The first line of the file should be the columns names, and it should contain the columns defined on the "parameters" during training.
- Input MIME type
- text/csv
https://github.com/NannyML/sagemaker_concept_shift_docs/blob/main/notebooks/data/synthetic_car_loan_reference.csv
https://github.com/NannyML/sagemaker_concept_shift_docs/blob/main/notebooks/data/synthetic_car_loan_analysis_with_targets.csv
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
y_pred | The values are the predicted labels.
Notice that the column name mapped to this column type on the "parameters" hyperparameter is defined during training, so you can have a different name for it on your CSV file. | Type: FreeText
Limitations: Data type can be text or integer | Yes |
y_pred_proba | The values are the predicted scores or probabilities for a specific class.
Notice that the column name mapped to this column type on the "parameters" hyperparameter is defined during training, so you can have a different name for it on your CSV file. | Default value: No default values
Type: Continuous | No |
y_true | This column type contains actual model targets.
Notice that the column name mapped to this column type on the "parameters" hyperparameter is defined during training, so you can have a different name for it on your CSV file. | Default value: No default values
Type: FreeText
Limitations: Data type can be text or integer | No |
feature_column_names | The list of column names for the features our model uses. | Type: FreeText
Limitations: The values are the features of your model. These can be categorical or continuous. NannyML identifies this based on their declared pandas data types. | Yes |
Resources
Vendor resources
Support
Vendor support
If you have any questions, reach out to support@nannyml.com
AWS infrastructure support
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