| Feature | | |
|---|---|---|
| Zero-config model build & deploy | V | X |
| Data source integration | Snowflake, MongoDB, BigQuery, Athena, Redshift and more | GCP |
| Multi-cloud support | AWS, GCP | GCP |
| Intuitive UI | V | X |
| Support | 24/7 by ML engineering experts | Standard GCP 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.
While offering a robust set of features, Vertex AI has a steeper learning curve, especially for those not already familiar with Google Cloud Platform. The platform is feature-rich but may require navigating through various services and configurations.
| Feature | | |
|---|---|---|
| Model build system | V | X |
| Model deployment & serving | V | V |
| Real-time model endpoints | V | V |
| Model auto scaling | V | V |
| 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.
Using Vertex AI in production demands a broad skill set, including ML engineering, containerization, Kubernetes orchestration, Infrastructure as Code (with tools like Terraform or Google Cloud Deployment Manager), and networking (VPC, firewall rules). Additional GCP services like Google Cloud Storage, Google Kubernetes Engine (GKE), and Google Cloud Monitoring add complexity, requiring diverse engineering skills for effective management.
| Feature | | |
|---|---|---|
| Managed feature store | V | V |
| Vector database | V | V |
| Batch features | V | V |
| Realtime features | V | V |
| Streaming features | V | Engineers required |
| Streaming aggregation features | V | Engineers required |
| 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.
Vertex AI is partially managed, meaning some services are fully managed while others may require manual setup. For example, AutoML is fully managed, but custom training and data pipelines might require additional configurations or integration with other GCP services.
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