Announcements

Qwak Vector Store simplifies your vector storage, retrieval, and embedding operations. At Qwak, we enable data science teams to focus on what they do best by eliminating the burden of infrastructure management. We handle everything for you, from scaling and data ingestion to embedding model deployment. With just a simple click, you can effortlessly deploy any embedding model and begin ingesting data into vectors.

Qwak Workspaces combine the power of Jupyter Notebooks with the convenience of managed environments, allowing data science teams to innovate faster on their machine learning projects.

Qwak is named one of the 20 Rising Stars as part of the eighth annual Forbes 2023 Cloud 100 list, the definitive list of the top 100 private cloud companies in the world, published by Forbes in partnership with Bessemer Venture Partners and Salesforce Ventures.

We’re thrilled to announce the release of our new features: Enhanced Overview Dashboard and Model Instance Sizes. These features make model deployment easier and more efficient than ever before.

We have an exciting update for all our customers! Starting July 2023, we are thrilled to announce that Qwak is now available on the AWS Marketplace.

Real-time features are features that are being calculated on the request time instead of being calculated in advance from a defined data source, such as BigQuery, Kafka, etc. As such, the raw data of the requested feature should arrive in the request itself, or be accessed directly from the feature set definition (code) - for example, making calls to external APIs or ad-hoc data fetching from a database.

With Qwak 2.0, you can bid farewell to the time-consuming tasks associated with machine learning operations. Our platform streamlines and automates crucial processes, freeing up your valuable time and resources and allows automatic scalability.

We are extremely excited to announce Qwak’s new ML model management and feature store enhancements. These new capabilities allow Qwak’s users to optimize ML model performance and deliver top grade machine learning production workflows.

Machine models need to be frequently deployed to support changes such as data drift, data decay and new target audiences. A deployment strategy is a way to update an existing ML model deployed in production and support the continuous testing and management of multiple models simultaneously.

Qwak now supports deploying machine learning (ML) models with event-driven streaming architecture using Apache Kafka to support high-throughput predictions.

Qwak now supports machine learning models feedback tracking.

Qwak Feature Store now supports Snowflake as data source

Qwak Feature Store now supports MongoDB as data source