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Tatiana Verbitskaya

Tatiana Verbitskaya

Tatiana Verbitskaya is a Solutions Engineer dedicated to solving complex problems. In addition to her client-facing work at Keymakr, she is also an active contributor to the computer vision community.

Annotating robot demonstration data

Annotating robot demonstration data

Instead of manually programming robot behavior, robots are trained using human demonstrations, teleoperation sessions, and multimodal interaction data. This approach allows robots to learn complex manipulation, navigation, and decision-making skills. At the heart of this are simulation learning and scalable annotation datasets. They help machine learning models understand which

Physical AI Training Data: How to Build Datasets for Robots That Interact with the Real World

Physical AI Training Data: How to Build Datasets for Robots That Interact with the Real World

The development of AI is increasingly moving beyond the digital realm. While traditional AI systems have primarily worked with text, images, and online data, a new generation of technologies — physical AI — interacts with the real world through robots, autonomous machines, drones, and intelligent industrial systems. Such systems must perceive the

Creating robust benchmark datasets

Creating robust benchmark datasets

Even sophisticated machine learning systems can produce misleading results if tested on poorly designed or inconsistent datasets. Therefore, robust benchmark datasets and carefully designed evaluation frameworks are the foundation of modern AI development. Modern AI systems rely on golden dataset methodologies, structured test set generation strategies, and standardized evaluation metrics

Key Metrics for Measuring LLM Performanc

Key Metrics for Measuring LLM Performanc

The performance evaluation of large language models extends far beyond simply checking whether answers are correct, because these systems operate in a space of subjectivity and probabilities. The main challenge is that a model can generate grammatically flawless and highly convincing text that, upon closer analysis, turns out to be

Continual Learning with LLMs: Adapting Models to New Information

Continual Learning with LLMs: Adapting Models to New Information

In a dynamic world where knowledge is constantly updated, there is a need for approaches that enable models to learn continuously without complete retraining. Continuous learning aims to overcome these limitations by allowing models to integrate new knowledge while preserving what they already have. One of the key problems in

The complete guide to LLM API integration

The complete guide to LLM API integration

Whether you're building a chatbot, automating workflows, or adding semantic search, API integration is the bridge between your product and AI capabilities. Below, we'll cover everything from REST API basics and model endpoints to SDK integration, embedding API usage, and architecture patterns. Quick Take * API integration

LLM Data Compliance: Privacy and Regulatory Guide

LLM Data Compliance: Privacy and Regulatory Guide

The development of large language models has reached a stage where technological advantage is inextricably linked to the legal purity of processes. The specifics of training and operating such systems involve processing colossal amounts of information, which automatically transforms every model into a potential source of confidential data. While artificial