Özge Aykaç
Özge is an industry analyst at AIMultiple focused on data loss prevention, device control and data classification.
She is a member of the AIMultiple DLP benchmark team and evaluates the effectiveness of the top DLP providers.
Latest Articles from Özge
Data Labeling for NLP with Real-life Examples
NLP technology is increasingly being used to enable smart communication between people and their devices. Companies like Google, Amazon, and OpenAI have invested billions in NLP technologies that can understand, interpret, and generate human language with remarkable accuracy. However, behind every sophisticated NLP model lies an important foundation: labeled training data.
Top 5 Open Source MDM Software
Mobile devices are a significant source of business data breaches. While some companies require sophisticated closed-source MDM software, others prefer open-source solutions to protect their devices.
Digital Transformation for Telecoms with Case Studies
The telecommunication or telecom sector is a ~$1.5 trillion market that makes communication possible worldwide. As remote work becomes more widespread, consumer needs continuously change in the telecom sector, demanding better services from telecom service providers. Like every other sector, the telecommunications sector can also benefit from digital transformation to improve its services.
7 Steps to Obtain Computer Vision Training Data
Computer vision (CV) technology is advancing rapidly in various industries. As demand for computer vision systems rises, so does the need for well-trained models. These models require large, high-quality, accurately labeled datasets, which can be costly and time-consuming to collect.
Automated Data Collection Tools & Use Cases
Automated data collection involves using automated systems to gather, process, and analyze information efficiently. Since automated data is produced from multiple sources and comes in various formats, understanding the different types of data and their origins is crucial for effectively implementing data automation.
Top 6 AI Data Collection Challenges & Solutions
AI adoption was slightly lower last year (Figure 1); one reason could be the various challenges in implementing AI. Training data collection has been identified as one of the main barriers to AI adoption. To avoid data-related challenges, businesses are opting to work with AI data collection services.
LLM Data Guide & 6 Methods of Collection
In the expanding AI and generative AI market (Figure 1), large language models (LLMs) have emerged as pivotal. These models empower machines to generate human-like content, heavily reliant on quality data. Here, we present a guide for business leaders on accessing and managing LLM data, offering insights into collection methods and data collection services.
Top 4 Field Agent Competitors & Alternatives
Monitoring retailers across different countries can pose a challenge for manufacturers of CPGs (consumer packaged goods) and FMCGs. Planogram audit services or retail audit companies offer solutions to help overcome these challenges. Field Agent is a service provider specializing in retail monitoring services for consumer packaged goods (CPG) producers.
Top 4 Facial Recognition Data Collection Methods
Despite the controversies surrounding this technology, the facial recognition systems (FRS) market continues to grow. Facial recognition applications are everywhere, from helping improve mental disorder diagnoses to finding fugitives. Developing and improving these systems requires facial data, which sometimes can be challenging to obtain due to security and privacy-related concerns of people.
Audio Annotation
A subset of data annotation, audio annotation, is a critical technique for building well-performing natural language processing (NLP) models. These models offer numerous benefits to organizations, including analyzing text, speeding up customer responses, and recognizing human emotions. In this article, we take a deep dive into audio annotation to understand its importance for businesses.
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