Why Data Science Engineers are Talking about Qwak’s Platform

August 12 19:57 2022

At least 56% of businesses now report AI adoption in their processes, as ML implementation crosses the Gartner Hype Cycle’s peak of inflated expectations. Of course, these numbers would be higher, but complex operations such as model deployment stand in the way of progress.

Data Science engineers say ML and AI model deployment is the most complex process in enterprise ML. It is such a rigorous process that over many enterprises expend 50% of their data scientists’ time on the model deployment phase.

This process becomes more daunting with scale. ML model deployment is a backbreaking process because most enterprises place more focus on ML code development, a very tiny part of the entire model production process.

However, the machine learning development process requires input from various teams during production. Additionally, it is a probabilistic process that calls for vigilance and maintenance since unanticipated and random events will occur.

Consequently, MLOps requires organizational change and access to various monitoring and observability tools and data stores.

How Qwak eases the complexity of the model deployment process

Qwak is a leading machine learning operations platform that unites processes, teams, and technology, reducing the model deployment process for data science engineers. Qwak’s robust MLOps features are scalable, automated, reliable, and minimize team friction.

The Build System, for instance, can help transform a line of code into production-grade solutions through its formidable “traditional” build processes. The build system will standardize your project’s structure and help data science engineers generate auditable and retrainable models.

It will also automatically version all your model’s code, data, and parameters, building deployable artifacts. On top of that, its model version tracks disparities between multiple versions, warding off data and concept drift.

Data science engineers that use Qwak have access to its Model Serving feature that supports the quick deployment of scalable models to production. They can set up their models via Qwak Serving’s one-click deployment services and auto-scale as per predefined metrics. 

Furthermore, the Model Serving places all your model’s logs, metrics, and performance data in one place. Consequently, your DevOps engineers, data scientists, and software engineers can easily participate in your business’s machine learning life cycle.

The Qwak Inference Lake is an off-the-shelf service that offers MLOps teams’ central storage of baseline data, feedback, and inference data from all groups and for all build models. The Inference Lake’s other formidable features include its model data audit, observability, and performance management features.

Qwak’s Co-founder and CEO, Alon Lev, believes that machine learning in production is navigable. So, along with other Qwak co-founders Yuval Fernbach and Ran Romano, Lev has engineered Qwak to simplify the ML model productization experience.

Consequently, data science engineers and teams from Guesty, JLL, Yotpo, and NetApp now use Qwak to ease the deployment of their ML models. As a result, these teams now deploy tens of models in a short period. In the past, they could only deploy a model or two after months of backbreaking work due to the complexity of the deployment process.

“We love Qwak because it provides a unified, end-to-end solution for managing ML-based applications in production,” says Jonathan Yaniv of Yotpo.

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