Arize Phoenix
Arize Phoenix is an open-source AI observability and evaluation platform developed by Arize AI for tracing, evaluating, and debugging large language model (LLM) and agent applications.
MLOps
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AutoML (Automated Machine Learning)
AutoML (Automated Machine Learning) is the automation of the end-to-end pipeline of applying machine learning to real-world data, replacing manual trial and error with a formal search over data preprocessing…
Baseten
Baseten is an inference platform for deploying, serving, and scaling machine learning models in production.
BentoML
BentoML is an open-source Python framework for packaging, serving, and deploying machine learning and AI models as production inference services.
Concept drift
Concept drift is the change over time in the statistical relationship between a model's inputs and its target, formally when the joint distribution P(X, Y) (and in the most damaging case the conditional P(Y |…
Data-centric AI (DCAI)
Data-centric AI (DCAI) is the discipline of systematically engineering and improving the data used to train a machine learning model, rather than holding the data fixed and iterating mainly on model…
Distributed training
Distributed training is the practice of training a single machine learning model using many compute devices in parallel, splitting the data, the model, or both across GPUs, TPUs, or other accelerators that…
Dynamic model
A dynamic model in machine learning is a model that is retrained frequently or continuously as new data arrives, so that its parameters track changes in the underlying data distribution over time.
Feature store
A feature store is a centralised data system that stores, serves, discovers, shares, monitors and reuses machine-learning features, separating feature computation from model training and inference so the same…
Google Vertex AI
Google Vertex AI is the unified machine learning and generative artificial intelligence platform offered by Google Cloud, announced at Google I/O on May 18
Helicone
Helicone is an open-source LLM observability platform and AI gateway founded in 2023 by Justin Torre, Cole Gottdank, Barak Oshri, and Scott Nguyen.
Inference
In machine learning, inference is the execution of a trained model to produce an output from an input.
KAI Scheduler
KAI Scheduler is an open-source Kubernetes scheduler that optimizes the allocation of GPU resources for artificial intelligence and machine learning workloads.
Kubeflow
Kubeflow is an open-source MLOps platform that runs the entire machine learning lifecycle on Kubernetes, described by its creators as a project "dedicated to making using ML stacks on Kubernetes easy, fast and…
LangSmith
LangSmith is a commercial observability, evaluation, and deployment platform for large language model (LLM) applications and AI agents, developed and operated by LangChain Inc. It provides developers and…
Langfuse
Langfuse is an open-source LLM engineering platform that provides observability, tracing, prompt management, evaluation, and dataset tooling for applications built on large language models.
Lightning AI
Lightning AI is a New York based artificial intelligence platform company founded by William Falcon, the creator of the pytorch lightning deep learning framework.
MLOps
MLOps (Machine Learning Operations) is a set of practices, principles, and tools for deploying, monitoring, and maintaining machine learning models in production reliably and efficiently.
MLflow
MLflow is an open-source platform for managing the end-to-end machine learning lifecycle, covering experiment tracking, model packaging, a model registry, deployment, and (since 2025) generative-AI…
Mistral Forge
Mistral Forge, branded by its developer simply as Forge, is an enterprise platform and engineering service from Mistral AI for building custom large language models and related multimodal models on an…
Model deployment
Model deployment is the MLOps process of taking a trained machine learning model and making it available in a production environment so it can serve predictions to applications, users, or downstream systems.
NVIDIA Picasso
NVIDIA Picasso is a cloud-based generative AI foundry from NVIDIA for building, training, and deploying visual generative models that produce images, video, and 3D content from text prompts.
Neural architecture search
Neural architecture search (NAS) is a technique for automating the design of neural network architectures.
Offline inference
Offline inference (also called batch inference, static inference, or bulk scoring) is the practice of running a trained machine learning model over a known set of inputs ahead of time and storing the resulting…
Online inference
Online inference (also called dynamic inference, real-time inference, or on-demand prediction) is the practice of running a trained machine learning model synchronously inside a request path
Operation (op)
In machine learning, an operation (often abbreviated as op) is a basic computational unit that manipulates data, typically tensors, during the training or execution of a model.
Parameter Server (PS)
The Parameter Server (PS) is a distributed system architecture for training large machine learning models in which one set of machines, the server nodes, holds the global model parameters as a partitioned…
Partitioning strategy
A partitioning strategy in distributed deep learning is the plan that decides how a model and its training data are split across multiple accelerators (typically GPUs or TPUs) so that the workload can be…
Patronus AI
Patronus AI is an automated LLM evaluation, observability, and guardrails platform founded in 2023 and headquartered in San Francisco.
Pipeline
A pipeline in machine learning is a sequence of data processing steps chained together into an automated, end-to-end workflow, where each step takes the previous step's output as its input.
Pipelining
Pipelining is a term used in two distinct senses within machine learning and artificial intelligence.
Post-processing
In machine learning, post-processing is any operation applied to a model's raw outputs after the prediction step but before the results reach a downstream system or end user.
Ray Serve
Ray Serve is a scalable, framework-agnostic model serving library built on top of the Ray (framework) distributed computing system.
Replicate
Replicate is a cloud platform for running, deploying, and sharing machine learning models via a simple API.
Run:ai
Run:ai (legal name Runai Labs Ltd.) is an Israeli software company that developed a Kubernetes-based orchestration and scheduling platform for graphics processing unit (GPU) resources used in artificial…
SavedModel
A SavedModel is the standardized, language-neutral serialization format that TensorFlow uses to package a complete, trained model (its computation graph, its weights, and any supporting assets) as a…
Serving
In machine learning, serving (also called model serving or inference serving) is the process of deploying a trained model into a production environment so it can answer prediction requests, usually exposed as…
Static inference
Static inference is a machine learning serving pattern in which a model generates a batch of predictions offline, ahead of time, and caches them so that applications read the precomputed answer from storage…
TensorFlow Serving
TensorFlow Serving (often shortened to TF Serving) is Google's open source system for serving machine learning models in production: it loads trained models, exposes them over gRPC and REST APIs, and manages…
Training-Serving Skew
Training-serving skew is a difference between a machine learning model's performance during training and its performance during serving (production inference).