Concordia is a part of a suite of open-source machine learning packages that allow organizations to more rapidly develop and deploy machine learning models.
Installation
pip install concordia
Description
Concordia is a tracking and analytics tool for machine learning models running in production.
Using Concordia, you should be able to rapidly have confidence in your shipped ML models.
If everything’s working as expected, you should have quick proof that you’re in a position to scale up this model.
If things are not going according to plan, you should be able to see that rapidly, and have a suite of information to hone in on the root cause of those discrepancies.
Basic Setup
from concordia import Concordia concord = Concordia() ml_predictor = load_ml_model() concord.add_model(model=model, model_id='model123')
Basic Usage
In your training environment
The goal here is to save the features and predictions as you calcate them in your training environment. Then, we can compare these to the features and predictions coming from your live environment. We use the row_ids to match rows across the two environments.
df = load_my_data() ml_predictor = train_ml_model() predictions = ml_predictor.predict(df) concord.add_data_and_predictions(model_id='model123', features=df, predictions=predictions, row_ids=df['my_row_identifier'])
In your live environment
from concordia import load_concordia concord = load_concordia() data = get_live_data() prediction = concord.predict(model_id='model123', features=data, row_id=data['my_row_identifier'])
In your analytics environment
from Concordia import load_concordia concord = load_concordia() concord.analyze_prediction_discrepancies(model_id='model123') concord.analyze_feature_discrepancies(model_id='model123')
Infrastructure Assumptions
Concordia relies on MongoDB and Redis. These can be either local, or in the cloud. You can specify DB credentials and connection options when creating and loading Concordia.
Database Configuration
You can easily specify your own DB connection. You’ll need to do this both when creating the Concordia instance in the first place, as well as when you load_concordia() to get access to that same Concordia instance later.
persistent_db_config = {
'db': '__concordia_test_env'
, 'host': 'localhost'
, 'port': 27017
}
in_memory_db_config = {
'db': 8
, 'host': 'localhost'
, 'port': 6379
}
concord = Concordia(in_memory_db_config=in_memory_db_config, persistent_db_config=persistent_db_config)
# To load this instance of Concordia later, use that same persistent_db_config
concord = load_concordia(persistent_db_config=persistent_db_config)
What does Concordia do, under the hood?
Metadata
Release files for concordia 0.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| concordia-0.1.1.tar.gz | 12.2 kB | Without provenance | Details for concordia-0.1.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| concordia-0.1.1-py2.py3-none-any.whl | Python 2, Python 3 | none | any | Details for concordia-0.1.1-py2.py3-none-any.whl |
Total release size: 31.5 kB
Release files / concordia-0.1.1.tar.gz
| Download URL | concordia-0.1.1.tar.gz |
|---|---|
| Size | 12.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
b7f65ee43dbe38718b0702fede8383d321401d3d489d47c8929ef2b5dbd238a8
|
|
BLAKE2b-256 checksum How to use checksums |
4a07ceea2b4efa997445d5b2fd32e515505e4d2298b1846c82a39e74ba1eee52
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
Release files / concordia-0.1.1-py2.py3-none-any.whl
| Download URL | concordia-0.1.1-py2.py3-none-any.whl |
|---|---|
| Size | 19.2 kB |
| Tags | Python 2 Python 3 |
|
SHA-256 checksum How to use checksums |
c9309a95cb3944cfe4ece2d281fc750de6cab6e6b5b016cac8674c80f39319f3
|
|
BLAKE2b-256 checksum How to use checksums |
0b0c7a8a04dde866f4a78dbd61beaee793f468b34965fdb80a95a9303684c828
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
Security for concordia 0.1.1
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.