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(Modified National Institute of Standards and Technology) is a widely used benchmark dataset in machine learning, consisting of 70,000 grayscale images of handwritten digits (0–9). It is primarily used for training and testing image processing systems, particularly in tasks like...

MLOps(Machine Learning Operations) is a set of practices, tools, and methodologies designed to streamline the deployment, monitoring, and maintenance of machine learning (ML) models in production environments. It combines principles from DevOps, data engineering, and ML to ensure the scalable,...

METEOR **(Metric for Evaluation of Translation with Explicit ORdering)**is a metric used to evaluate the quality of machine-generated text, such as translations or summaries, by comparing them to human reference outputs. It improves upon traditional metrics like BLEU by incorporating...

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,...

LLM Monitoring is the process of continuously tracking the performance, behavior, and outputs of Large Language Models (LLMs) during real-world deployment. This practice ensures that the models maintain reliability, relevance, and alignment with user expectations, while also detecting and addressing...

LLM Evaluation refers to the systematic assessment of Large Language Models (LLMs) to measure their performance, reliability, and alignment with desired objectives. This process employs a variety of metrics and methodologies to evaluate the quality of outputs, identify limitations, and...