We built it. Now, you can take your models to production more seamlessly & collaboratively.
Qwak is built to allow autonomous environment for Data Science and engineers.
we built Qwak for production, and we know that things can sometimes go wrong, so we created a solution that is fully reproducible
Any data science can take his model to a live managed end point without being dependent in other dev functions
Analytics is an out of the box solution in Qwak, no additional effort is required.
Our feature store elements all the data dependencies between data science and engineers We will manage the scale
In fact, one of the main pain points we identified while building Qwak is the friction between data scientists and engineers. This friction is the main reason models stay on the drawing board.
We built Qwak to reduce and even eliminate the dependency between data scientists and engineers. We seek to accomplish this goal by allowing each stakeholder to independently reach a point where the other feels comfortable enough to take the model to the next level, as every stakeholder has everything they need to manage and use their relevant part of the ML models.
One of the worst attempts at “selling” is “ to enjoy the benefits, you must first invest X human months.” Qwak turns this sentence on its head. We built Qwak so that you can build the solution that fits your needs with the tools that YOU feel comfortable using! We will never ask you to change the way you work, add automation tools that you don't like, or monitor your models in a way that doesn’t make sense to you.
In a nutshell, Qwak is a “Production ML Platform.” Our founders leverage massive production experience and knowledge about both research and real-world environments - and how they don’t always mesh as seamlessly as one might hope. We are here to make sure you have everything you need to take your models from the research phase to the real world. You will build it; we will take care of the rest.
Train, test, serialize, and containerize your application using one simple command - from your CLI or your existing workflow orchestration tools
Push changes of all relevant types — including new features, configurations, models, data changes — into production in a safe and quick manner.
Using just a simple decorator in your code, automatically record every prediction made to your prediction API.
Track model health metrics such as throughput, latency, error rate and more. Configure alerts on top of them in a single
Create a single, curated, discoverable source of truth for ML features. Allow data scientists and engineers to collaborate and share features between projects and models. Ensure features are served in production the same way they were used in training.