Qwak - Skip Complex MLOps. Build ML.

Data Stack
DS/ML PlatformsDS/ML Tooling
Status
Growth
Summary

Qwak is a centralized platform that streamlines the entire machine learning development lifecycle, allowing teams to easily build, manage, and deploy ML models at any scale, while focusing on business value without the burden of infrastructure concerns.

Who's using?
SaltGuestyCirkulNotionUpsideJLLYotpoOpenWebLightricksliliNetAppHelloHeartWindwardCyraSpot

Qwak streamlines the entire ML development lifecycle with a single platform.

Easily build ML projects at any scale using a centralized platform that contains everything you need to build ML.

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The world’s best machine learning teams have chosen Qwak

Time to move forward from endless integrations

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Old ML architecture

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yellow line over ML architecture

“Qwak streamlines ML development from prototype to production, freeing us from infrastructure concerns and maximizing our focus on business value.”

Software Engineer, Notion

One platform that allows you to focus on what matters

Model Registry

Manage models in one place for research and production

Model Training

Train models of any size with a single click

Model Serving

Deploy models to production at any scale in just one click

Model Monitoring

Monitor model performance and data anomalies

Feature Store

Easily manage all your features in one place

Vector Store

Store and ingest embedding vectors at any scale

Feature + Vector Pipeline

Transform data into features and vectors at any scale

Managed Notebooks

Experiment with managed Jupyter notebooks

Qwak integrates with all stagesof the model lifecycle

Explore Integrations

HuggingFace

Snowflake

Kafka

BigQuery

Jfrog

Qwak optimizes ML models in production

Ryan Rollings

Principal Machine Learning Engineer

“We ditched our in-house ML platform for Qwak. I wish we had found them sooner.”

Edward Zhou

Software Engineer

“Qwak streamlines ML development from prototype to production, freeing us from infrastructure concerns and maximizing our focus on business value.”

Idan Benaun

Director of ML and Data Science

“People ask me how I managed to deploy so many models while onboarding a new team within a year. My answer is: Qwak.”

Filip Gvardijan

Data Science Manager

“With Qwak, our ML team efficiently manages and deploys various models, both batch and real-time. The addition of an observability and Vector DB layer has been a game-changer, allowing us to confidently bring 10 models into production. Qwak's robust and streamlined approach has significantly enhanced our operational efficiency.”

Idan Schwartz

Head of Research

“Before Qwak, delivering a new ML model took weeks... Now the research team can work independently and deliver while keeping the engineering and product teams happy.”