| Feature | | |
|---|---|---|
| Zero-config model build & deploy | X | X |
| Data source integration | GCP | Multiple data sources |
| Multi-cloud support | GCP | AWS, GCP, Azure |
| Intuitive UI | X | X |
| Support | Standard GCP support | Standard |
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.
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 | X | Engineers required |
| Model deployment & serving | V | V |
| Real-time model endpoints | V | V |
| Model auto scaling | V | Engineers required |
| Model A/B deployments | Engineers required | Engineers required |
| Inference analytics | Engineers required | Engineers required |
| Managed notebooks | V | V |
| Automatic model retraining | Engineers required | Engineers required |
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.
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 | Engineers required | V |
| Streaming aggregation features | Engineers required | Engineers required |
| Online and offline store auto sync | X | Engineers required |
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.
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.
Don’t just take our word for it
Qwak was brought onboard to enhance Lightricks' existing machine learning operations. Their MLOps was originally concentrated around image analysis with a focus on enabling fast delivery of complex tabular models.
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