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Ntropy
Enabling products without data barriers.
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Using Elyra to create Machine Learning pipelines on Kubeflow
Using Elyra to create Machine Learning pipelines on Kubeflow
Robin Kahlow
Apr 27
Customized Transaction Classification
Customized Transaction Classification
Successes, benchmarking, and how to maximize your performance. Bringing meta-learning to production.
Jonathan Kernes
Apr 19
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…
Louis Outin
Apr 3
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…
David Buchmann
Mar 31
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
Chady Dimachkie
Mar 19
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
Bagrat Aznauryan
Feb 25
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
Jonathan Kernes
Feb 20
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…
Thymo ter Doest
Feb 4
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…
David Buchmann
Jan 24
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…
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…
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…
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…
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.
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.
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…
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…
Ilia Zintchenko
Apr 24, 2020
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