Databricks on Azure VMs
Rating: 7 out of 10
IncentivizedUse Cases and Deployment Scope
I used Azure Virtual Machines in my last organization for deploying out Machine Learning model and related workloads on virtual machines. Our requirement was to enable automated deployment of our compute engine - Databricks, our ML models, and Airflow workflows on scalable virtual machines and Azure Virtual Machines was our choice in the last organization I worked with.
Pros
- Rapid Scalability
- Variety of elastic storage options
- Flexibility and control for app deployment
- Regular Updates for security and feature upgrades
- Fault tolerance
- Native Integration with Databricks
Cons
- Pricing can be a bit better
- Compute types can be increased (AWS EC2 has more)
- No Bare metal GPU instances as in OCI
Likelihood to Recommend
The VM deployment process is really simple in Azure Virtual Machines. But as I said earlier, compute types were a bit limited when I used it. In a few scenarios we had requirements for a Bare Metal GPU instance for high performance compute, but it wasn't available, so we had to look for alternatives.
