Search Results
15 results for “mlops”
AutoML
AutoML (Automated Machine Learning) is the process of automating the selection, composition, and tuning of machine learning algorithms and pipelines, enabling practitioners to build effective models with reduced manual effort.
Data Pipeline
A data pipeline is an automated sequence of processes that ingests, transforms, and delivers data from source systems to destination systems for analysis, machine learning, or operational use.
DataOps
DataOps is an engineering methodology that applies agile, DevOps, and lean manufacturing principles to data pipelines, aiming for rapid, reliable, and repeatable delivery of analytics and machine learning data.
DataRobot
An American enterprise AI platform company that provides automated machine learning, model deployment, monitoring, and generative AI tools, headquartered in Boston, Massachusetts.
Feature Store
A centralised data platform for storing, serving, and managing machine learning features so that they can be reused consistently across training and online inference.
Google Vertex AI
Google Vertex AI is a unified machine learning platform on Google Cloud that consolidates data preparation, model training, deployment, and monitoring for both custom-built models and Google's foundation models including Gemini.
Langfuse
Langfuse is an open-source LLM engineering platform that provides observability, tracing, prompt management, evaluation, and dataset tooling for teams building applications on top of large language models.
MLflow
An open-source platform for managing the end-to-end machine learning lifecycle, including experiment tracking, model packaging, a model registry, and deployment.
MLOps
A set of practices and tools that combine machine learning, DevOps, and data engineering to automate and operationalise the full lifecycle of ML models from development through production deployment and monitoring.
Model Compression
Model compression is a set of techniques that reduce the size, memory footprint, and computational cost of machine learning models while preserving predictive accuracy, enabling deployment on resource-constrained hardware.
Model Registry
A model registry is a centralised system that catalogues, versions, and governs trained machine learning models throughout their lifecycle, supporting reproducibility, deployment, and compliance.
Model Serving
Model serving is the discipline of deploying trained machine learning models behind APIs or runtimes so that production applications can request predictions at scale with predictable latency, throughput, and reliability.
ONNX (Open Neural Network Exchange)
An open standard format for representing machine learning models that enables interoperability between deep learning frameworks, runtimes, and hardware platforms.
Synthetic Data
Synthetic data is artificially generated data that mimics the statistical properties of real datasets, created using generative AI or simulations to train machine learning models without exposing sensitive personal information.
Weights and Biases
Weights and Biases (W&B) is a machine learning developer platform for experiment tracking, model versioning, dataset management, and collaborative model evaluation used by over 200,000 ML practitioners worldwide.