Superior ML demands
Superior infrastructure

A fully managed platform that unifies ML engineering and data operations - providing agile infrastructure that enables the continuous productionization of ML models at scale.

Streamline ML productionization, Increase ML output

Qwak removes the engineering friction from deploying machine learning products while allowing fast iterations, high scale and customizable infrastructure

One platform, many use cases, zero friction.

Build

Qwak build system adds “traditional” build processes to machine learning (ML) models and allows data scientists to build an immutable and tested production-grade artifact.

Qwak build system standardizes an ML project structure that automatically versions data, code, and parameters for every model build.

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Serving

Qwak Serving allows deployment of scalable models to production with one click, which reduces the friction between data science and engineers.

Qwak Serving enables teams to deliver prediction services in a fast, repeatable, and scalable way, including advanced metrics, logging, and alerting capabilities.

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Data Lake

Qwak Lake is a fully managed data lake that comes as an off-the-shelf product with the Qwak Platform. In Qwak Lake you can find all your inference, feedback, and baseline data for each model and its different versions (aka builds).

The Qwak Lake data can be accessed directly via the Qwak SQL interface on the Analytics page, or via any other query or data processor engine.

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Feature Store

Qwak’s Feature Store allows data scientists (DSs) and machine learning (ML) engineers to collaborate effectively and quickly among themselves and with the R&D organization. It’s an easy way to develop features using batch and real-time data sources and serve them in production instantly. Discover and reuse available feature sets for their entities, instead of re-creating the same or similar ones.

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Automation

Qwak Automations allows configuring triggers based on all the different model “layers,” infrastructure, data, and statistics. They also allow you to run actions — whether they’re Qwak internal like triggering a Qwak Build (retrain and log version) & deployment, or external (like calling external APIs and integrating with third-party applications). Using Qwak Automation allows you to make sure your models are always under watch and can also heal themselves and revert to  below the threshold you’ve defined. Qwak Automation is crucial when models are part of the production environment.

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Qwak Platform

Build, Serve, maintain, and monitor
ML models and features in a single platform.

Build
Transform ML code to a production-grade ML solution.
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SERVING
Manage, deploy, and serve your ML models at scale.
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Data Lake
Collect, store, and analyze your models data.
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Feature Store
Manage data for machine learning.
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automation
Automate machine learning processes.
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Our Customers

Machine learning leaders are talking about Qwak

Oren Neiberg
Machine Learning Engineer

“With Qwak we were able to improve our ML delivery dramatically.Qwak has allowed us to work to the highest engineering standards from day one and to invest the majority of our efforts in our business challenges and not into plumbing.”

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Or Hiltch
VP of Engineering

“Using Qwak allowed us to focus on creating a business impact rather than spending valuable time on our infrastructure setup. At JLL our development is very time sensitive. As a result of implementing Qwak, we improved our execution time by 4.5X.”

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Jonathan Yaniv
Data Science Leader

“We love Qwak because it provides a unified, end-to-end solution for managing ML-based applications in production.
Yotpo has a wide variety of data science needs and Qwak can accommodate them all with ease. Qwak is robust, scalable, simple yet flexible - exactly what we were looking for.”

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See Qwak in Action

Let one of our machine learning architects guide you through a tailored demo
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