Manage your own Feature Store on Databricks with Hopsworks
Wednesday July 15th @8:00 PM CEST
Sign up now →
15

Banking & Finance

The Promise of
Deep Learning for AML

The key insight of deep learning for AML is that deep neural networks (DNNs) can generalize from training data to identify patterns in transactions that are indicative of fraud. To be more specific, Generative Adverserial Networks (GANs) have been shown to be able to descriminate between normal money transfer patterns and anomalous money laundering situations by using historical patterns and graph-based models of money transfers. This makes it harder for money launderers to make small changes in how they transfer money to circumvent the existing static set of money laundering rules that are currently used to alert investigators of transactions potentially involved in money laundering.

Existing rule-based systems for identifying transactions that may involve money laundering consist of thousands of rules. They generate an alert when a rule matches for a transaction - suspecting the transaction to involve money laundering. These systems generate a huge numbers of false-positive alerts (alerts where the transaction did not involve money laundering) that take time and money to chase down.


Governance & Compliance

Hopsworks is built for Enterprises. Read the Product sheet for Hopsworks Enterprise to how it provides:

- TLS-Based Security for Data-in-Transit;
- Full Audit-trail support, Encryption for Data-at-Rest;
- Integration with Active Directory, LDAP, OAuth2;
- Project-based multi-tenancy, enabling data to be shared and processed in a cluster environment;
- Provenance support for Machine Learning Pipelines - enabling fully reproducible models;
- Conda environments & Pip Libraries in Air-gapped deployments;

 Hopsworks Product Sheet

Hopsworks at a glance

Efficiency & Performance

Development & Operations

Governance & Compliance

Feature Store
Data warehouse for ML
Distributed Deep Learning
Faster with more GPUs
HopsFS
NVMe Speed with Big Data
Horizontally Scalable
Ingestion, Dataprep, training, Serving
Notebooks For development
First-class Python Support
Version Everything
Code, Infrastructure, Data
Model Serving on Kubernetes
TF Serving, MLeap, SkLearn
End-to-End ML Pipelines
Orchestrated by Airflow
Secure Multi-tenancy
Project-based restricted Access
Encription At-rest, In-Motion
TLS/SSL everywhere
AI-Asset Governance
Models, Experiment, data, GPUs
Data/Model/Feature Lineage
Discover/track dependencies

Efficiency & Performance

Feature Store
Data warehouse for ML
Distributed Deep Learning
Faster with more GPUs
HopsFS
NVMe Speed with Big Data
Horizontally Scalable
Ingestion, Dataprep, training, Serving

Development & Operations

Notebooks For development
First-class Python Support
Version Everything
Code, Infrastructure, Data
Model Serving on Kubernetes
TF Serving, MLeap, SkLearn
End-to-End ML Pipelines
Orchestrated by Airflow

Governance & Compliance

Secure Multi-tenancy
Project-based restricted Access
Encription At-rest, In-Motion
TLS/SSL everywhere
AI-Asset Governance
Models, Experiment, data, GPUs
Data/Model/Feature Lineage
Discover/track dependencies

Book a demo

Get an introduction to Hopsworks and Hopsworks Feature Store for your Machine Learning projects together with one of our engineers.

A comprehensive walk-through
• How Hopsworks can align with your current ML pipelines
• How to manage Features within Hopsworks feature store
• The benefits of Hopsworks Feature Store for your teams

Let us know if your specific wishes and pre-requisites for your personal demonstration.