| Feature | | |
|---|---|---|
| Zero-config model build & deploy | X | V |
| Data source integration | AWS data sources | Snowflake, MongoDB, BigQuery, Athena, Redshift and more |
| Multi-cloud support | AWS | AWS, GCP |
| Intuitive UI | X | V |
| Support | Standard AWS support | 24/7 by ML engineering experts |
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.
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.
| Feature | | |
|---|---|---|
| Model build system | X | V |
| Model deployment & serving | V | V |
| Real-time model endpoints | Engineers required | V |
| Model auto scaling | Engineers required | V |
| Model A/B deployments | Engineers required | V |
| Inference analytics | Engineers required | V |
| Managed notebooks | V | V |
| Automatic model retraining | Engineers required | V |
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.
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.
| Feature | | |
|---|---|---|
| Managed feature store | V | V |
| Vector database | V | V |
| Batch features | Engineers required | V |
| Realtime features | Engineers required | V |
| Streaming features | Engineers required | V |
| Streaming aggregation features | X | V |
| Online and offline store auto sync | X | V |
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.
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.
Don’t just take our word for it
OpenWeb, a social engagement platform that builds online communities around better conversations, needed a way to scale their expanding data science team’s efforts and show immediate value.
Read Case Study
