Azure AI Studio Can Replace Your AIOPs Development Stack
Use Cases and Deployment Scope
As in AI-service based organizations, Azure AI Studio has been a gamechanger from the start. The scope of our use cases where Azure AI Studio literally boosts us off the chart is the speed in deploying ML models via the notebooks, building MLOPs pipeline and scheduling them. In recent years, LLMs are also now available for deployment and even building custom ones on them.
Pros
- Wide Catalog of Models is a beautiful feature for all who want to evaluate a lot of models before proceeding with any client use case for the best performance.
- Playground for testing and evaluating visual comparisons on so many metrics like latency, cost, and output time.
- Integration with other Cloud services, this makes the full complete solutioning and vision complete, from storage to compute.
Cons
- Model Catalog can have a feature to show basic compute and the cost of running the model on that compute. With latency metrics, I generally need to do a lot of research before losing some dollars on deployment on hit and trials.
- Documentation generator for pipelines deployed in notebooks, generally developers use notebooks for experimentation, where logging them can be a big overhead.
Return on Investment
- Onboarding a team member for the codebase is slightly slower, almost 20% slower. As codebase sharing is like a git pull from repos, whereas here we need to provide all the access.
- I have experienced scaling up speed almost 50% faster as per compared with on-prem solutions. ML models are faster deployed in terms of on-prem deployments.
- 10 times better Azure AI Studio for cost visibility over any other solution.
Usability
Alternatives Considered
Gemini Enterprise Agent Platform
Other Software Used
Domino Enterprise MLOps Platform, Gemini Enterprise Agent Platform
