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Michael Abramov

Michael Abramov

Curating Datasets for Underwriting and Risk Assessment with AI

Curating Datasets for Underwriting and Risk Assessment with AI

Combining traditional financial analysis with innovations such as autonomous vehicle safety modeling is changing data processing strategies. The shift from manual processes to automated solutions requires structured, validated information that delivers accurate predictions. Integrating machine learning accelerates analysis, but success depends on data preparation. Prioritize standardization and cleansing to reduce

Automated annotation for self-optimizing networks

Automated annotation for self-optimizing networks

Mobile networks today handle millions of signals every second. These signals include everything from user activity to system performance, and together, they create an overwhelming amount of raw data. Going through such information line by line would be impossible for a human engineer. Automated annotation solves this problem by sorting

How to Label Aerial and Drone Images for Power Line Inspection

How to Label Aerial and Drone Images for Power Line Inspection

Categorized visuals are the dream of autonomous detection tools. By dividing the content into three classes, real-world patterns, simple patterns, and dual patterns, the teams create AI that adapts to different infrastructure patterns. A 70/30 split between training and validation content ensures that the models generalize without overtraining. Combining

Artificial Intelligence for Smart Grids

Artificial Intelligence for Smart Grids

Traditional power grids, built for one-way power flow, are now suffering from the variability of solar and wind power, peak usage spikes, and aging infrastructure. Modern infrastructure requires real-time decision-making based on AI models trained on labeled operational data. These systems analyze weather conditions, consumer behavior, and equipment performance to

Federated Learning Annotation Pipelines Explained

Federated Learning Annotation Pipelines Explained

Centralized systems that collect raw user data have become a liability and regulatory risk. New laws like GDPR and California’s CCPA have forced organizations to change how they handle sensitive information. Enter a privacy-first alternative that changes how AI models gain intelligence. Rather than pooling datasets in a single

How to Fine-Tune Enterprise LLMs on Proprietary Data

How to Fine-Tune Enterprise LLMs on Proprietary Data

Fine-tuning large language models (LLMs) on proprietary enterprise data has become essential for achieving domain-specific performance, especially in regulated or knowledge-heavy industries. General-purpose LLMs often fall short when dealing with specialized terminology, internal procedures, or compliance requirements. The tuning process typically begins with selecting a base model and applying quantization

Annotation Practices for Transformer-Based Architectures

Annotation Practices for Transformer-Based Architectures

It is well known that most errors in AI systems arise from poor data preparation. This highlights a critical gap that is often overlooked: rigorous labeling processes adapted for modern neural networks. In this section, we explore how rigorous workflows improve machine learning results. We examine how raw input data