Machine Learning
25 items tagged with "machine-learning"
Best Practices2
Feature Store Best Practices
A centralized system for defining, storing, and serving machine learning features consistently for training and inference, avoiding skew and duplicated work.
MLOps Principles
The discipline of applying DevOps and engineering rigor to machine learning so models are built, deployed, monitored, and retrained reliably and reproducibly.
Reference Architectures1
Stacks3
MLOps Stack
MLflow, Kubeflow, TensorFlow, Feature Store - ML lifecycle
MLflow MLOps Stack
End-to-end MLOps pattern using MLflow for experiment tracking, model registry, packaging, and deployment, integrated with feature, data, and serving layers.
Feast Feature Store Stack
Feature store pattern using Feast to define, materialize, and serve consistent ML features from an offline warehouse and a low-latency online store.
FAQs7
What is a large language model (LLM)?
A large language model is a neural network trained on vast amounts of text to predict the next token in a sequence, which lets it generate and underst...
What are embeddings in machine learning?
Embeddings are dense numerical vectors that represent text, images, or other data in a continuous space where semantic similarity corresponds to geome...
What is fine-tuning a model?
Fine-tuning is the process of continuing to train a pre-trained model on a smaller, task-specific dataset so it adapts to a particular domain, style, ...
What is inference in machine learning?
Inference is the phase where a trained model is used to make predictions or generate output on new inputs, as opposed to training where the model lear...
What is MLOps?
MLOps is a set of practices for reliably building, deploying, monitoring, and maintaining machine learning systems in production, applying DevOps prin...
What is the difference between supervised and unsupervised learning?
Supervised learning trains a model on labeled examples, where each input has a known target, so the model learns to predict labels for new data in tas...
What is overfitting in machine learning?
Overfitting happens when a model learns the training data too closely, including its noise and quirks, so it performs well on that data but poorly on ...
Glossaries11
Large Language Model (LLM)
A large language model is a neural network trained on vast text corpora to predict the next token, enabling it to generate and understand natural language across many tasks.
Token
A token is the basic unit of text an LLM processes, typically a word fragment, whole word, or character, produced by a tokenizer and mapped to a numeric ID.
Tokenization
Tokenization is the process of splitting raw text into tokens that a model can map to numeric IDs, usually using a subword algorithm such as byte-pair encoding.
Embedding
An embedding is a dense numeric vector that represents the meaning of text, an image, or other data so that similar items sit close together in vector space.
Fine-Tuning
Fine-tuning is the process of further training a pretrained model on a smaller, task-specific dataset to specialize its behavior, style, or domain knowledge.
Inference
Inference is the process of running a trained model on new inputs to produce outputs, as opposed to the training phase that creates the model.
Training
Training is the process of adjusting a model's parameters from data so it learns to perform a task, typically by minimizing a loss function with gradient descent.
Transformer
A transformer is a neural network architecture built around self-attention that processes sequences in parallel, forming the basis of modern large language models.
Attention Mechanism
An attention mechanism lets a model weigh the relevance of different parts of its input when producing each output, focusing on the most pertinent tokens.
Quantization
Quantization reduces the numeric precision of a model's weights and activations, shrinking memory use and speeding inference with limited accuracy loss.
Foundation Model
A foundation model is a large model pretrained on broad data at scale that can be adapted, through fine-tuning or prompting, to a wide range of downstream tasks.