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.
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.
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.
Data Quality Assurance with Best Practices
Optimal decisions require high-quality data. To achieve and sustain high data quality, companies must implement effective data quality assurance procedures. See what data quality assurance is, why it is essential, and the best practices for ensuring data quality.
Top 20 Data Labeling Tools
Data labeling, the process of annotating raw data (such as images, text or audio), is essential for training ML models to perform tasks like classification and recognition. Here, we introduce top 20 data labeling tools. The top data labeling tools: Ranking: From most to least comprehensive.
10 Open Source Data Labeling Platforms
Data labeling, the process of annotating raw data (such as images, text, or audio), is essential for training ML models to perform tasks like classification and recognition. While pre-built solutions exist, they may not always meet specific needs, making open-source platforms a more flexible and customizable alternative. See the top 10 open-source data labeling tools.
12 Digital Transformation Trends & Use Cases in Education
The COVID-19 pandemic has accelerated digital transformation in education as nearly 1.5 billion students worldwide became distanced from their classrooms. However, online education is not the only way digital technologies transform the teaching and learning experience. We explore how digital transformation affects education with key technologies and trends.
Top 20 Analytics Case Studies
Although the potential of Big Data and business intelligence are recognized by organizations, Gartner analyst Nick Heudecker says that the failure rate of analytics projects is close to 85%. Uncovering the power of analytics improves business operations, reduces costs, enhances decision-making, and enables the launching of more personalized products.
7 AI Transformation Strategies
AI transformation is the next phase of digital transformation. Businesses are willing to invest in AI technologies to stay ahead of competitors. Digital transformation is a prerequisite for companies to initiate their AI transformation, as digital data is essential for AI training, and digital processes are typically required to deploy AI solutions.
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