| Feature | | |
|---|---|---|
| Zero-config model build & deploy | V | X |
| Data source integration | Snowflake, MongoDB, BigQuery, Athena, Redshift and more | Multiple data sources |
| Multi-cloud support | AWS, GCP | AWS, GCP, Azure |
| Intuitive UI | V | X |
| Support | 24/7 by ML engineering experts | Standard |
Designed with a user-friendly interface, Qwak aims to make the MLOps process as straightforward as possible. The platform is built to be intuitive, allowing users to focus on machine learning tasks without the distraction of complex configurations.
Databricks leverages open-source tools like Apache Spark, MLflow and Airflow, which offer a lot of configurability but can be complex for some users. While it provides a robust set of features for big data analytics, it may lack specific out-of-the-box ML features, requiring users to build custom solutions using Spark. This adds a layer of complexity and requires a deeper understanding of the underlying technologies.
| Feature | | |
|---|---|---|
| Model build system | V | Engineers required |
| Model deployment & serving | V | V |
| Real-time model endpoints | V | V |
| Model auto scaling | V | Engineers required |
| Model A/B deployments | V | Engineers required |
| Inference analytics | V | Engineers required |
| Managed notebooks | V | V |
| Automatic model retraining | V | Engineers required |
Qwak is designed to abstract away most of the engineering complexities, allowing data scientists and ML engineers to focus on what they do best: building and deploying models. The platform handles everything from data storage to model monitoring, reducing the need for specialized engineering skills.
Databricks, a cloud-based platform integrating with various providers, heavily relies on Apache Spark for data processing. While it manages some infrastructure aspects, users need a good grasp of Spark configurations. In contrast. Databricks' deployment time varies; simple models can take minutes, but complex scenarios may extend to days, particularly without prior Spark experience. This variability, while enhancing flexibility, introduces complexity impacting deployment speed.
| Feature | | |
|---|---|---|
| Managed feature store | V | V |
| Vector database | V | V |
| Batch features | V | V |
| Realtime features | V | V |
| Streaming features | V | V |
| Streaming aggregation features | V | Engineers required |
| Online and offline store auto sync | V | Engineers required |
Qwak provides a fully abstracted environment, allowing users to focus on ML tasks without worrying about the underlying infrastructure. It supports both CPU and GPU instances and can run on AWS or GCP.
Databricks offers a Feature Store that supports batch data sources and allows for feature transformations using Spark SQL or PySpark functions. Features can be stored in both an Offline and Online Store but require manual schema definition. While it supports a range of data sources, it is optimized for the Databricks ecosystem. Streaming data sources and streaming aggregations are not natively supported.
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