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Ntropy

Enabling companies to understand their customers through financial data.

The anatomy of our ML production system: the infrastructure (Part 1)

The anatomy of our ML production system: the infrastructure (Part 1)

Follow us on the journey of how we provisioned our ML infraustructure for production at Ntropy and what we learned along the way!
Go to the profile of Jorge Pessoa
Jorge Pessoa
Feb 13
Ntropy Core Principles

Ntropy Core Principles

We went through a cultural reset recently and had to rethink and rephrase who we are and what we stand for. I thought sharing this publicly…
Go to the profile of Naré
Naré
Feb 8
Bag of tricks for optimizing machine learning training pipelines

Bag of tricks for optimizing machine learning training pipelines

Go to the profile of Arseny Kravchenko
Arseny Kravchenko
Jan 5
Herding Entities

Herding Entities

Ntropy works in the business of “transaction intelligence”. At its core, lies enrichment of financial transactions. With the Ntropy SDK…
Go to the profile of Marcin Ziemiński
Marcin Ziemiński
Dec 2, 2022
The False Promise of General Transaction Categorization and the inadequacy of in-house models

The False Promise of General Transaction Categorization and the inadequacy of in-house models

Why the Ntropy customized model approach is the only solution for high performance
Go to the profile of Jonathan Kernes
Jonathan Kernes
May 24, 2022
Using Elyra to create Machine Learning pipelines on Kubeflow

Using Elyra to create Machine Learning pipelines on Kubeflow

Go to the profile of Robin Kahlow
Robin Kahlow
Apr 27, 2022
Customized Transaction Classification

Customized Transaction Classification

Successes, benchmarking, and how to maximize your performance. Bringing meta-learning to production.
Go to the profile of Jonathan Kernes
Jonathan Kernes
Apr 19, 2022
Speeding up cross-encoders for both training and inference

Speeding up cross-encoders for both training and inference

At Ntropy (we’re hiring), we perform categorization on financial transactions over more than 150 different categories. We use NLP models…
Go to the profile of Louis Outin
Louis Outin
Apr 3, 2022
How to Protect yourself from Advanced Phishing Scams (HTML injection)

How to Protect yourself from Advanced Phishing Scams (HTML injection)

This is a guest post by Ben Chinoy, a talented security engineer. He reached out to us about the security issue outlined below and…
Go to the profile of David Buchmann
David Buchmann
Mar 31, 2022
Multilingual Named Entity Recognition for Financial Transactions

Multilingual Named Entity Recognition for Financial Transactions

How we were able to achieve the best accuracy for multilingual named entity recognition at Ntropy for financial transaction enrichment
Go to the profile of Chady Dimachkie
Chady Dimachkie
Mar 19, 2022
Hiding endpoints from SwaggerUI while supporting with Connexion

Hiding endpoints from SwaggerUI while supporting with Connexion

Maintaining backwards compatibility is one side of the coin. The other issue is to make sure the new customers use only the latest version
Go to the profile of Bagrat Aznauryan
Bagrat Aznauryan
Feb 25, 2022
Quickly finding recurring events in massive sequences

Quickly finding recurring events in massive sequences

A scalable O(N*K) solution for sequences of size N and max intervals of size K
Go to the profile of Jonathan Kernes
Jonathan Kernes
Feb 20, 2022
Clustering millions of sentences to optimize the ML-workflow

Clustering millions of sentences to optimize the ML-workflow

At Ntropy, we deal with a large volume of financial transactions represented as plain text. We train models on these transactions from…
Go to the profile of Thymo ter Doest
Thymo ter Doest
Feb 4, 2022
How we reduced our infrastructure costs by 8x without sacrificing performance

How we reduced our infrastructure costs by 8x without sacrificing performance

This blog post explains our journey in MLOps, describing how we leveraged the combination of open-source technologies and cloud providers…
Go to the profile of David Buchmann
David Buchmann
Jan 24, 2022
Deep ML cannot be built in-house

Deep ML cannot be built in-house

We have heard multiple arguments over the last years that ML should be built in-house. From investors, customers, books and even candidates…
Go to the profile of Ilia Zintchenko
Ilia Zintchenko
Dec 27, 2021
Transaction data is an uncut gem

Transaction data is an uncut gem

Transactional data is generated every time money is transferred between two entities, one initiating the transaction order and the other…
Go to the profile of Naré
Naré
Dec 21, 2021
A castle getaway: the Ntropian offsite

A castle getaway: the Ntropian offsite

I am often asked about the why behind Ntropy. The truth is that it takes time to figure out your real why and what makes you tick. There is…
Go to the profile of Naré
Naré
Dec 12, 2021
Understanding financial transactions

Understanding financial transactions

The term “transaction categorization” is widely used in financial services and in the broader software industry and is well-known in…
Go to the profile of Naré
Naré
Dec 12, 2021
The Ntropy API

The Ntropy API

The Ntropy API enables companies to understand the financial transactions of their customers with super-human accuracy.
Go to the profile of Ilia Zintchenko
Ilia Zintchenko
May 31, 2021
No-data ML

No-data ML

No-data ML allows models to obtain their data externally and be deployed to production without any in-house labels.
Go to the profile of Ilia Zintchenko
Ilia Zintchenko
May 20, 2021
Dissolving data silos

Dissolving data silos

The benefits of bridging data silos have recently gained significant attention by organizations of all sizes. In 2018, Google established…
Go to the profile of Ilia Zintchenko
Ilia Zintchenko
Apr 24, 2020
The unreasonable effectiveness of combining datasets

The unreasonable effectiveness of combining datasets

A machine learning model generally gets more and more accurate with more data. However, each new observation also adds less and less new…
Go to the profile of Ilia Zintchenko
Ilia Zintchenko
Apr 24, 2020
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