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Feature Engineering & Indicators · Reviewed 27 Oct 2026

getML

Automated feature engineering for relational and time series data.

free tier · getml.com ↗

Paid against free › Feature Engineering & Indicators › getML

getML at a glance
1.9KEstimated total trafficModeled monthly visits34Ranking keywordsPublic search sample88/100Technical health2 pages checked994Referring domainsLive backlink summary

Overview

01

Summary

The useful answer before the supporting detail.

getML is a high-performance machine learning software for predictive analytics on relational and time series data S1. It is used by data scientists and ML teams to automate feature engineering, addressing the challenge of manually building feature pipelines S1. The suite provides a Python API, Engine, and Monitor, enabling users to generate billions of features with a few lines of code S1 S48. Its practical value lies in reducing coding overhead and improving model performance without extensive manual effort S34.

02

Product identity and positioning

Name, category, positioning, audience, and first-party claims.

  • Identity: getML, a software suite for automated feature engineering on relational and time series data S48.
  • Core purpose: Solves the problem of manual feature engineering and complex feature pipelines S1.
  • Intended users: Data scientists and ML teams working with enterprise relational or time series data S34.
  • Product scope: A suite comprising Python API, Engine, and Monitor, with Community and Enterprise editions S48.
  • Positioning: Positioned as a high-performance solution that brings supervised learning to raw relational data S1.
  • Claimed distinction: Claims to surpass other propositionalization libraries and reduce coding overhead S34.
03

Use cases and audiences

Who the product serves and the jobs it is designed to handle.

AudiencePractical use case
Data scientistsAutomate feature engineering to build predictive models without manual feature logic S34.
ML teamsAccelerate development timelines by reducing collaboration overhead between data scientists and domain experts S34.
EnterprisesApply relational learning to complex enterprise data for tasks like customer churn prediction S34 S39.
ResearchersUse advanced feature learners like Relboost for high-dimensional medical datasets S39.

Features

09

Features and capabilities

Core functions and evidence-backed product capabilities.

CapabilityWhat it does
Automated feature learningGenerates features from relational data using supervised learning algorithms like FastProp and Relboost S1.
Python APIProvides an easy-to-use interface for building and managing ML pipelines S1 S63.
Data model abstractionsSupports StarSchema and TimeSeries models to capture relationships in data S10 S31.
Pipeline managementBundles data model, feature learners, and predictors into a single pipeline S31.
Database connectivityConnects to databases like Greenplum, MariaDB, MySQL, and PostgreSQL S10.
Model deploymentEnables deployment via REST APIs and cloud platforms like Google Cloud Vertex AI S39.
Feature drift robustnessDesigned to maintain performance despite changes in data patterns S34.
10

Integrations and ecosystem

Connected products, exports, APIs, and workflow compatibility.

Integration or interfaceTypeWhat it enables
Python APIAPIProvides a convenient interface to the getML Engine for building ML pipelines S63.
FastAPIIntegrationAllows creating REST APIs for getML models S39.
Google Cloud Vertex AIIntegrationEnables deployment of getML models on Google Cloud Platform S39.
Databases (Greenplum, MariaDB, MySQL, PostgreSQL)IntegrationConnects to various databases for data I/O S10.
DockerDeploymentSupports containerized deployment of the Engine S48.

Pricing

11

Pricing and plans

Published plans, free access, trials, credits, and pricing limitations.

Plan or offerVerified price and billingIncludedLimits
Community$0, not applicable billing S1FastProp feature engineering S1Elastic License 2.0 S1
EnterpriseContact sales, billing not published S1Adds Relboost and other relational learning algorithms S1Same Python API S1

Competition

12

Competitive landscape

Domains competing for the same organic search demand.

Sampled competitors

CompetitorFirst-page appearancesSampled queriesAverage positionQueries observed
medium.com214.5xgboostclassifier
github.com215reflect cpp
datacamp.com111xgboostclassifier
docs.vertica.com112xgboostclassifier
rfl.getml.com112reflect cpp
analyticsvidhya.com113xgboostclassifier
reddit.com113reflect cpp
conan.io114reflect cpp
gitee.com115reflect cpp
xgboost.readthedocs.io116xgboostclassifier

Website intelligence — Collected evidence

Customers, testimonials, and first-party review claims

No supporting public-page evidence was collected for this category.

Independent review sources discovered

No links to supported independent review platforms were discovered on the collected pages.

Website intelligence — Scout synthesis

First-party claims (from getml.com):

  • getML is described as "A game changer in predictive applications for enterprise applications" W7W31.
  • It is positioned as "The better solution for predictive analytics on your enterprise data" W16W40.

Scout inference: The homepage makes comparative claims but does not name specific competitors or provide direct comparisons.

14

AI visibility

Measured ChatGPT and Google AI mentions with cited sources.

AI-assistant referral traffic

  • Estimated current generative-AI referral share: 1.7%

Scout traffic model did not return a separate AI-referral trend series for this domain.

Website intelligence — Collected evidence

Site-stated AI capabilities

Page typeEvidenceSource
integrationsPowering the world’s best machine learning pipelines.https://getml.com/latest/
integrationsMachine Learning for enterprise datahttps://getml.com/latest/
integrationsgetML is a high-performance machine learning software for predictive analytics on relational and time series data. At the heart of getML's innovation are five novel feature learning algorithms that automate manual feature engineering using supervised learning. getML enables the creation of end-to-end prediction pipelines capable of learning from terabytes of raw relational data, achieving unparalleled model accuracy within days not months.https://getml.com/latest/
integrationsPowering the world’s best machine learning pipelines.https://getml.com/latest/
integrationsMachine Learning for enterprise datahttps://getml.com/latest/
integrationsgetML is a high-performance machine learning software for predictive analytics on relational and time series data. At the heart of getML's innovation are five novel feature learning algorithms that automate manual feature engineering using supervised learning. getML enables the creation of end-to-end prediction pipelines capable of learning from terabytes of raw relational data, achieving unparalleled model accuracy within days not months.https://getml.com/latest/

These are first-party product claims, not independent measurements of AI-search visibility.

15

Marketing opportunities

Actions calculated directly from the collected evidence.

OpportunityRecommended actionEvidence
Striking-distance keywordMove “xgboost regressor” from position 13 toward the top three.Volume 390 · https://getml.com/latest/reference/predictors/xgboost_regressor/
Important pageStrengthen the page already earning search visibility before creating an unrelated page.https://rfl.getml.com/supported_formats/capnproto/ · 3 sampled keywords · best position 17
Competitor gapReview the sampled queries where medium.com appears.2 first-page appearances across xgboostclassifier

Website intelligence — Collected evidence

Website evidence gaps

  • pricing: no usable public page was collected; verify whether the site publishes this evidence.
  • features: no usable public page was collected; verify whether the site publishes this evidence.
  • customers: no usable public page was collected; verify whether the site publishes this evidence.
  • reviews: no usable public page was collected; verify whether the site publishes this evidence.
  • changes: no usable public page was collected; verify whether the site publishes this evidence.
  • identity: no usable public page was collected; verify whether the site publishes this evidence.
  • technical: no usable public page was collected; verify whether the site publishes this evidence.

Website intelligence — Scout synthesis

First-party claims (from getml.com):

  • getML is used across industries, from finance and manufacturing to healthcare and beyond W15W39.
  • It is used by "next-gen startups to established enterprises" W10W34.
  • The company is hiring, with open positions in Leipzig/DE W2W26.

Scout inference: The homepage highlights broad industry applicability and a customer base ranging from startups to enterprises, which could be leveraged for targeted marketing campaigns. The hiring banner suggests company growth.

Trust and proof

16

Customers, proof, and reviews

Published proof, customer signals, and review evidence.

Independent reviews

No citation-grounded independent review links were found.

Customer proof

No named customers, testimonials, or customer case studies were found.

Independent mentions

  • Engine - getML Developer Portal — Engine - getML Developer Portal ... # getml.engine ... This module is a collection of utility functions for the overall communication and the session management of the getML Engine. In order to log into the Engine, you have to open your favorite internet browser and enter http://localhost:1709 in the navigation bar. This tells it to connect to a local TCP socket at port 1709 opened by the getML Monitor. This will only be possible from within the same device! ... ``` launch( allow_push_notificati
  • Engine CLI - getML Developer Portal — You do not need to launch getML Engine with the command line if you installed getML Suite using `pip` on Linux. You can execute getml.engine.launch() inside Python code to launch the Engine. On Linux, getML Engine can also installed and used via the command line interface (CLI) called `getML`. Some parameters can be set via command line flags. If you do not explicitly set them, the values from your config.json are taken instead. The config.json is located in `$HOME/.getML/getml-VERSION`. For Ent
  • Feature engineering — - getML community is an open source tool for automated feature engineering on time series and relational data ... It has been shown to be at least 60 times faster than tsflex, tsfresh, tsfel, featuretools or kats.
  • Feature engineering — - getML community is an open source tool for automated feature engineering on time series and relational data. 26 ... It is implemented in C/ C++ with a Python interface. ... 60 times faster than tsflex, tsfresh, tsfel, featuretools or kats. 29
  • SARDONYX-sard/awesome-stars — Breath of the Wild (Switch 1.5.0) - getml/reflect-cpp - A C++20 library for fast serialization, deserialization and validation using reflection. Supports JSON, Avro, Boost Serialization, BSON, Cap'n Proto, CBOR, Cereal, CSV, flexbuffers, msgpack, parquet,
  • App — The namespaced API for `@react-native-firebase/ml` has been removed. This package is modular-only — use `getML(app?)`. ... (modular) / / --- / --- / / `firebase.ml()` / `getML()` or `getML(app)` / / Default export `ml()` / `getML()` or `getML(app)` / /
  • "Pro ASP.NET MVC 3 Framework" study notes twenty-eight View 2 - Programmer All — Redirecting to a Literal URL The most basic way to redirect the browser is to call the Redirect method, which returns an instance of the RedirectResult class. For example: public RedirectResult Redire...

External links were grounded in one live web search. Search snippets are leads, not independently scraped page content; rejected links remain available in Review evidence.

17

Company, security, and trust

Ownership, policies, security signals, and public trust evidence.

SignalFindingSource
Company and ownershipPart of Code17 GmbH, our team of 10+ engineers in Leipzig and Munich is committed to delivering exceptional software solutions. From concept to deployment, we guide business leaders through the process, offering continuosource
Contact and supportContact us for more information.source
Legal and privacyNot published on the collected pages.—
Security and complianceNot published on the collected pages.—
Uptime and status pageNot published on the collected pages.—
Domain registered2011-07-09—
Domain expires2027-07-09—

Not found on the collected pages: legal and privacy, security and compliance, uptime and status page. This only means Scout did not find it on the pages it read; it says nothing about the company itself.

18

Documentation and product updates

Help resources, product guidance, and visible changes over time.

Resource or updateTypeWhat is availableDate or freshness
getML Developer PortalDocumentation... (source)Not dated
Changelog - getML Developer PortalChangelogPublic product change history. (source)Not dated
Contact and supportSupportPublic contact and support channel. (source)Not dated
Careers - getML Developer PortalDocumentationDocumentation is publicly available. (source)Not dated
About - getML Developer PortalDocumentationDocumentation is publicly available. (source)Not dated
getML Developer PortalDocumentationDocumentation is publicly available. (source)Not dated
Blog - getML Developer PortalDocumentationDocumentation is publicly available. (source)Not dated
Enterprise - getML Developer PortalDocumentationDocumentation is publicly available. (source)Not dated

Not found in the collected public sources: public roadmap, status page, separate help center or knowledge base.

Technical

19

Technical and delivery signals

Delivery model, platforms, API, data ownership, reliability claims and release activity.

SignalFindingSource
Delivery model (cloud, self-hosted, desktop, browser extension)Self-hosted via Docker or native Linux installation S48.S48
Platforms and appsLinux native and Docker containers S48.S48
API and extensibility (API, SDK, webhooks, MCP, plugins)Provides a Python API and integration with FastAPI for REST APIs S63 S39.S63 S39
Data ownership and exportNot published on the collected pages.—
Performance and reliability claimsClaims faster runtime and resource efficiency compared to competing tools S1 S39.S1 S39
Release activityChangelog documents ongoing updates and improvements S10.S10
20

Measured infrastructure

Domain, certificate, and hosting signals, measured directly.

SignalFinding
Domain registered2011-07-09 — INWX GmbH
Domain expires2027-07-09
TLS certificateLet's Encrypt, valid, expires 2027-01-21
Security headersnone served
Headers not servedHSTS, CSP, X-Content-Type-Options, X-Frame-Options, Referrer-Policy
Server headerGitHub.com
Mail handled byaspmx.l.google.com, alt1.aspmx.l.google.com
Nameserversmiki.ns.cloudflare.com, kanye.ns.cloudflare.com

Not measurable: wayback: rate limited by archive.org, ladder stopped after 1 probe(s). A gap here means the lookup was unavailable, not that the finding is negative.

21

Technical site audit

A bounded multi-page crawl with page-level issues and URLs.

Scout — Bounded technical crawl

  • Pages crawled: 2 of 15 maximum
  • Technical health: 88/100
  • Critical issues: 0
  • Warnings: 4
  • Notices: 2

Issues

SeverityIssueValueAffected URL
warningMissing meta description—https://getml.com/
warningPage should have one primary H10https://getml.com/
warningMissing canonical URL—https://getml.com/
noticeLow visible word count4https://getml.com/
warningMissing meta description—https://getml.com/latest/
noticeImages are missing alt text4https://getml.com/latest/

Crawled pages

StatusTitleH1sWordsResponseURL
200Redirecting0497 mshttps://getml.com/
200getML Developer Portal199592 mshttps://getml.com/latest/

This is a bounded Scout crawl. Core Web Vitals and Lighthouse scores are not claimed unless a dedicated performance audit is connected.

22

Search Console and Analytics

First-party search and behavioral data connection state.

Modeled acquisition channels

ChannelEstimated share
direct39.8%
search34.8%
search organic33.5%
search paid1.3%
social6.3%
social organic5.6%
social paid0.6%
referrals9.7%
paid referrals2.8%
mail3.1%
gen ai1.7%
affiliate2.0%
other0.0%

First-party connections

IntegrationStatusData available after connection
Google Search ConsoleNot connectedClicks, impressions, CTR, average position, queries, pages, countries, and devices
Google AnalyticsNot connectedOrganic sessions, engagement, landing pages, conversions, geography, and devices

No first-party Search Console or Analytics numbers are shown until the site owner authorizes the corresponding Google account. Scout traffic model values above are modeled estimates, not private analytics totals.

Limits and method

23

Limitations and open questions

Known constraints and missing evidence from the collected pages.

The collected pages do not mention specific competitors by name, but they compare against tools like featuretools and tsfresh in examples S39. Evidence gaps include lack of detailed pricing for Enterprise, no customer testimonials, and no security or compliance information. Open questions remain about the full feature set of the Enterprise edition and the specifics of the licensing terms.

24

Sources, method, and limitations

Evidence inventory, collection method, provider use, and constraints.

This report is based on 12 of 14 collected pages from getml.com, including documentation, changelog, examples, and installation guides. Static and browser rendering providers were used; deep page fetch and sitemap were disabled or unavailable. External proof search was performed but did not yield additional verified customer or review data. Limitations include incomplete pricing details and lack of third-party reviews.

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