AI eval and observability platform: tracing, LLM and human scoring, quality gates. Used by Vercel, Notion and Replit.
Results for “mlops”
25 tools found
Open-source feature store that manages the machine-learning data teams use for both model training and real-time predictions.
Managed feature store and AI lakehouse platform for building production machine-learning systems with millisecond-latency feature serving.
Serverless cloud platform that runs Python code, including AI model training and inference, on GPUs with sub-second startup and pay-per-second billing.
Edge orchestration platform that remotely manages, secures and updates AI inference and applications across thousands of distributed edge devices.
Enterprise cloud built specifically around NVIDIA GPUs, providing large-scale compute for training and running the biggest AI models.
Platform for labeling, organizing and quality-checking the images, video and sensor data used to train computer vision and robotics AI models.
Data annotation platform built for organizations running many labeling projects at once across images, video, text, PDF and geospatial data.
Data platform for labeling, curating and generating human feedback to train and fine-tune AI models, including reinforcement learning workflows.
GPU cloud built for AI research and production, scaling from a single rented GPU up to superclusters with over 165,000 GPUs.
Pay-by-the-second GPU cloud for training and running AI models, with no contracts and fast serverless cold starts.
Open-source tool that automatically finds and runs your AI training jobs on whichever cloud or cluster has the cheapest available GPUs.
Data annotation and evaluation platform that turns raw images, video, text and audio into labeled datasets for training and fine-tuning AI models.
A control room for every AI system a company uses — tracking what exists, what risk it carries, and whether it meets legal requirements — so compliance doesn't mean chasing spreadsheets across departments when an audit or new regulation shows up.
A platform that started by checking AI systems for unfair bias and has grown into a full compliance dashboard — finding AI a company didn't even know it was using, and tracking whether all of it meets legal and ethical standards.
A European, publicly-traded cloud built specifically for AI workloads — GPU clusters, training, inference — for companies that want serious AI compute without depending on a US hyperscaler.
A single API endpoint that talks to 600+ AI models from 30+ providers, so switching from one AI model to another — or spreading requests across several for cost or reliability — doesn't mean rewriting your app's code.
Generates realistic fake versions of your production data — same shape and statistics, no real customer information — so developers can test against data that looks real without ever touching actual user records.
A control panel for getting AI models and agents from a developer's laptop into real production use — deployment, scaling, and governance — built to run on whichever cloud a company already uses instead of locking them into one.
A marketplace where anyone with a spare gaming or server GPU can rent it out, so AI developers get GPU compute at a fraction of the price big cloud providers charge — like Airbnb, but for graphics cards instead of spare rooms.