Qwak
UnknownUnified MLOps and LLMOps platform for building, training, deploying, managing, and monitoring machine learning and generative AI applications at scale.
Informations sur l’outil
Qu'est-ce que Qwak ?
JFrog ML (formerly Qwak) is a single MLOps platform that enables teams to deliver production AI applications from idea to high scale. It covers the full lifecycle including feature engineering, model training and deployment, LLM application development, and real-time monitoring for both classic ML and GenAI/LLM workflows.
Fonctionnalités principales
Build and Manage Models
Centralize model management from research to production, enabling team collaboration, CI/CD integration, and visibility into training parameters and metadata.
Train and Fine-Tune Models
Train and fine-tune any model with one click on GPU or CPU machines, supporting all model types and enabling easy periodic retraining automation.
Deploy Models at Scale
Deploy models to production at any scale with one click as live API endpoints, batch inference on large datasets, or streaming models connected to Kafka streams, with multi-version deployments.
Real-Time Model Monitoring
Monitor model performance, detect data anomalies, track input data distribution shifts, and integrate with tools like Slack and PagerDuty for real-time health and performance tracking.
Prompt Management and Versioning
Manage prompts with a single registry for creation, production deployment, team collaboration, experimentation, version tracking, and a dynamic prompt playground.
LLM Model Library
Effortless one-click deployment of optimized open-source models such as Llama 3 and Mistral 7b on your cloud or the platform's, with automatic scaling.
LLM Workflows and Tracing
Create and visualize complex LLM flows with shadow prompt deployments for testing; trace and inspect requests, prompts, model inference calls, and latency for debugging.
LLM Application Monitoring
Monitor LLMs in production for performance and reliability, track metrics like response time, identify issues quickly, and integrate with Slack and PagerDuty.
Feature Store
Manage the entire feature lifecycle in one place for collaboration, consistency, and reliability in feature engineering and deployment.
Data Ingestion and Pipelines
Ingest data from warehouses and multiple sources, process and transform features, and build data pipelines with custom transformations.
Vector Store
Store embedding vectors at scale by ingesting data from any source, converting to embeddings, performing vector search, and supporting recommendation engines and RAG pipelines for LLM applications.
Cas d'utilisation
- #1
End-to-End MLOps for Classic ML
Build, train, deploy, and monitor traditional machine learning models from research to high-scale production in a unified platform.
- #2
LLM and GenAI Application Development
Develop, deploy, and manage LLM applications including prompt registries, optimized model libraries, complex workflows, tracing, and production monitoring.
- #3
Feature Engineering and Data Pipelines
Transform data, manage features across sources, and maintain a consistent feature store integrated with the AI platform.
- #4
Production AI Model Monitoring and Alerting
Track model and LLM performance, detect anomalies and data shifts, and receive real-time alerts via integrated tools.
- #5
RAG and Vector Search Applications
Build recommendation engines and retrieval-augmented generation pipelines using scalable vector storage and similarity search.
Plans tarifaires
Homepage provides Start for Free option along with links to Pricing and Schedule a Demo; no detailed plan or cost information is stated on the page.
Alternatives
Foire aux questions
What is Qwak?
What can I do with Qwak?
Who is Qwak designed for?
Does Qwak support both classic ML and LLMs?
What is the pricing for Qwak?
Which integrations does Qwak support?
Qwak Contacts et réseaux sociaux
- SociétéJFrog ML
- Site Internet :
- Twitter
- LinkedIn
- GitHub
Avis
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