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ease
features
design
support

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Description

ALBERT is a self-supervised Transformer architecture that undergoes pretraining on a vast dataset of English text, eliminating the need for manual annotations by employing an automated method to create inputs and corresponding labels from unprocessed text. This model is designed with two primary training objectives in mind. The first objective, known as Masked Language Modeling (MLM), involves randomly obscuring 15% of the words in a given sentence and challenging the model to accurately predict those masked words. This approach sets it apart from recurrent neural networks (RNNs) and autoregressive models such as GPT, as it enables ALBERT to capture bidirectional representations of sentences. The second training objective is Sentence Ordering Prediction (SOP), which focuses on the task of determining the correct sequence of two adjacent text segments during the pretraining phase. By incorporating these dual objectives, ALBERT enhances its understanding of language structure and contextual relationships. This innovative design contributes to its effectiveness in various natural language processing tasks.

Description

IBM InfoSphere® Optim™ Data Privacy offers a comprehensive suite of tools designed to effectively mask sensitive information in non-production settings like development, testing, quality assurance, or training. This singular solution employs various transformation methods to replace sensitive data with realistic, fully functional masked alternatives, ensuring the confidentiality of critical information. Techniques for masking include using substrings, arithmetic expressions, generating random or sequential numbers, manipulating dates, and concatenating data elements. The advanced masking capabilities maintain contextually appropriate formats that closely resemble the original data. Users can apply an array of masking techniques on demand to safeguard personally identifiable information and sensitive corporate data within applications, databases, and reports. By utilizing these data masking features, organizations can mitigate the risk of data misuse by obscuring, privatizing, and protecting personal information circulated in non-production environments, thereby enhancing data security and compliance. Ultimately, this solution empowers businesses to navigate privacy challenges while maintaining the integrity of their operational processes.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Amdocs Customer Experience Suite
Hadoop
IBM Cloud
IBM Db2
IBM InfoSphere Optim
IBM Informix
JD Edwards EnterpriseOne
Oracle PeopleSoft
Oracle Siebel CRM
SQL Server
Spark NLP

Integrations

Amdocs Customer Experience Suite
Hadoop
IBM Cloud
IBM Db2
IBM InfoSphere Optim
IBM Informix
JD Edwards EnterpriseOne
Oracle PeopleSoft
Oracle Siebel CRM
SQL Server
Spark NLP

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

github.com/google-research/albert

Vendor Details

Company Name

IBM

Founded

1911

Country

United States

Website

www.ibm.com/il-en/products/infosphere-optim-data-privacy

Product Features

Data Privacy Management

Access Control
CCPA Compliance
Consent Management
Data Mapping
GDPR Compliance
Incident Management
PIA / DPIA
Policy Management
Risk Management
Sensitive Data Identification

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