The Egyptian Plover: Rhymes with "over". An African waterbird that maintains a (dubious) symbiotic relationship with crocodies, feeding on decaying meat lodged between their teeth.

Automatically find, explain, and fix errors without rules.

Your organization's performance is limited by the quality of the data that underlies its learning and decision making. Redpoll Plover is the only AI data platform that bolts onto your data system to automatically find, explain, and mitigate bad data to help your organization perform at its peak potential.

What Plover can do:

Automatically identify erroneous values

Protect your dataset from future erroneous values

Identify the best data to collect for learning

How it works

1. Plover builds a model of your data

        from plover.source import ImmutableSqlSource
        from plover.store import AwsS3Store
        from plover.backend import AwsBackend
        from plover.engine import DatabaseEngine

        conn = connect_to_sql_database()

        db_engine = (
        DatabaseEngine(
        source=ImmutableSqlSource(conn, "SATELLITES_WH"),
        store=AwsS3Store("myorg-plover", "SATELLITES_WH"),
        backend=AwsBackend("myorg-plover"),
        )
        .fit()
        .persist()
        .metalearn()
        .persist()
        )
        ```

Holistic modeling and metalearning

Plover builds a holistic model of the world as defined by your data, then learns a metamodel to further refine its understanding of the information in your data.

2. Identify Likely Errors

        top_5_errors = (
        db_engine
        .metrics
        .errorness
        .sort(by=['Confusion'], descending=True)
        .head(5)
        )
        ```

Row	Column	Confusion	Observed	Predicted
Intelsat 903	Eccentricity	10.514456	0.793069999999…	0.000335826120…
Intelsat 902	Inclination_ra…	10.036763	25.06467339	0.002274957131…
Intelsat 903	Apogee_km	7.853968	358802	35792.92656399…
DSP 20 (USA 14…	Period_minutes…	6.695287	142.08	1436.095724064…
SDS III-6 (Sat…	Source_Used_fo…	4.93515	JM/5_11	ZARYA

Inconsistency is key

Plover finds errors by identifying data that are inconsistent with its model of your data or cause confusion.

Plover also shows you the observed value and its predicted value, which you may use to overwrite erroneous values.

3. Find similar errors

          errs_like = db_engine.errors_like(
          "DSP 20 (USA 149) (Defense Support Program)",
          "Period_minutes"
          )
          ```

row	rowsim	Observed	Predicted
SDS III-7 (Sat…	0.988281	23.94	1436.088006
SDS III-6 (Sat…	0.964844	14.36	1436.113453
Advanced Orion…	0.9453125	23.94	1436.105354

Metareasoning

After identifying an error, Plover can identify similar errors by finding data that are inconsistent or confusing in similar ways.

4. Identify errors in incoming data

        err = db_engine.detect(new_satellite_record)
        err[0]
        ```

Row	Column	Incon Quantile	Observed	Predicted
Satmex 8	Users	0.99998	Commecial	Commercial

Protect Prod

Compute how confusing or inconsistent data are before they make it into the database to protect production systems

5. Fill knowledge gaps

          to_fill = db_engine.find_missing_to_fill(
          to_help_predict="Purpose"
          )

          to_fill[0].show()
          ```

All (missing) data are not created equal

Plover can identify missing fields that are most likely to reduce uncertainty in specific predictions if filled in.

Machine learning + human learning + engineering

Baxter Eaves

CEO

Baxter is a US Navy veteran and holds a PhD in Experimental Psychology from the University of Louisville where he developed computational models of human trust and social learning. He has led a number of DARPA projects and brings 13 years of experience deploying human-inspired AI tech in high-risk industries.

Patrick Shafto

Scientist at large

Patrick is a program manager at DARPA under the Information Innovation office (I20) and professor of Data Sciences at Rutgers University - Newark. He has led a number of projects for agencies including DARPA, DOD, and NSF, and his publications have appeared in top journals of machine and human learning.

Michael Schmidt

Principal ML Engineer

Michael has 14 years of research and engineering experience. He has built production models for healthcare, agronomy, finance, and law; and has conducted research in the areas of high-energy physics, differential geometry, plasma physics, and high-performance computing.

For information cultivation

What it is not

There are many data quality and observabilty platforms out there and they all do one or more of the following:

focus on mechanical failures of your data architecture that can be completely avoided with solid database architecture i.e. you can do it yourself;
do anomaly/outlier detection by looking for distributional changes in single columns, which requires that a certain amount of bad data make it into prod in order to make the comparison and ignores the context of the data;
use error between predictions made by your machine learning models and the observed data as a measure of anomaly, which requires you to go through the effort of building an ML model that is more accurate than your data;
flatten your database, throwing out your carefully designed relational structure and biasing learning toward the entities that interact most with the database.

What it is

Plover focuses singly of improving the veracity of the values in your databases toward improving the information in your databases, and thus improving your ability to learn. By creating a model of your entire database (without flattening it) we are able to evaluate the erroneousness of each datum individually. This allows us to pluck out (or impute) individual bad data and protect prod without lag.

Get in touch

Provide your information here and we will reach out to you promptly.