Natural Language Processing (NLP) is a field of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. It combines linguistics, machine learning, and computational techniques to process and analyze natural language data, such as text...

Neural Network refers to a class of machine learning models inspired by the structure and function of the human brain. These networks consist of layers of interconnected nodes (neurons) that process and transform data to perform tasks like classification, prediction,...

Multimodal refers to machine learning systems that process and integrate information from multiple types of data, or “modalities,” such as text, images, audio, video, and structured data. Multimodal models aim to leverage the complementary strengths of different data types to...

MNIST Dataset is a widely used benchmark in machine learning and computer vision that contains 70,000 images of handwritten digits (0-9). Each image is a 28×28 grayscale pixel grid, making it a standard for evaluating the performance of image classification...

MLOps refers to a set of practices that combines machine learning (ML) and operations (Ops) to streamline the deployment, monitoring, and maintenance of ML models in production environments. By applying DevOps principles to machine learning workflows, MLOps ensures that models...

METEOR (Metric for Evaluation of Translation with Explicit ORdering) is an automatic evaluation metric used primarily for machine translation and text generation tasks. It aims to improve upon earlier metrics like BLEU by offering a more nuanced assessment of translation...

Machine learning is a subset of artificial intelligence (AI) that enables systems to learn from data, identify patterns, and make decisions without explicit programming. ML algorithms build models by processing input data and using statistical methods to improve their performance...

LLMOps refers to the set of tools, practices, and workflows designed to manage the deployment, monitoring, optimization, and maintenance of Large Language Models (LLMs) in production environments. It extends the concepts of MLOps (Machine Learning Operations) to address the unique...

LLM Safety refers to the practices and methodologies designed to ensure that Large Language Models (LLMs) operate responsibly, ethically, and without causing harm. It involves aligning LLMs with societal values, reducing biases, and mitigating risks like harmful content, misinformation, or...

LLM Observability refers to the practice of gaining in-depth visibility into the behavior, performance, and decision-making processes of Large Language Models (LLMs). It focuses on understanding how and why LLMs produce certain outputs, enabling developers to identify issues, optimize performance,...