| Feature | | |
|---|---|---|
| Zero-config model build & deploy | V | X |
| Data source integration | Snowflake, MongoDB, BigQuery, Athena, Redshift and more | AWS data sources |
| Multi-cloud support | AWS, GCP | AWS |
| Intuitive UI | V | X |
| Support | 24/7 by ML engineering experts | Standard AWS support |
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.
SageMaker, though powerful, demands a solid grasp of AWS and engineering expertise. Its UI is less intuitive than specialized platforms, requiring navigation and expertise through multiple AWS services.
| Feature | | |
|---|---|---|
| Model build system | V | X |
| Model deployment & serving | V | V |
| Real-time model endpoints | V | Engineers required |
| 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.
SageMaker does not have Training Jobs or simple deployment, and its Experiments feature and Studio IDE introduce complexity. The deployment and monitoring processes entail manual engineering setup, with limited out-of-the-box support.
| Feature | | |
|---|---|---|
| Managed feature store | V | V |
| Vector database | V | V |
| Batch features | V | Engineers required |
| Realtime features | V | Engineers required |
| Streaming features | V | Engineers required |
| Streaming aggregation features | V | X |
| Online and offline store auto sync | V | X |
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.
The AWS Sagemaker Feature Store requires manual setup for feature processes and lacks support for streaming aggregations, necessitating additional services like Elasticsearch, Chorma, Pinecone, and others for similar functionality.
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