Hi @chahatkumar00,
Welcome to Google Cloud Community!
It's great you're diving into AI/ML and exploring different deployment options on Google Cloud. Let’s break down your questions one by one to clear up the confusion.
1. When should we choose Vertex AI over a custom deployment? : Vertex AI is designed to simplify the process of building, deploying, and managing machine learning models at scale. It integrates well with other GCP services and provides built-in tools for training, tuning, and monitoring models. You should choose Vertex AI when:
- You need ease of integration: Vertex AI streamlines many processes, from model training to deployment. It’s especially helpful for users who prefer an integrated approach rather than managing all the components individually.
- You want a scalable, production-ready solution: Vertex AI is built for handling large-scale deployments. It automatically manages scaling, versioning, and model monitoring, which is really useful when the application needs to grow.
- You need advanced features like AutoML, Hyperparameter Tuning, and Experiment Tracking: These are built-in features of Vertex AI that help optimize models efficiently.
- In short, if you want to avoid manually configuring the infrastructure and want a more managed service for ML tasks, Vertex AI is a good choice.
2. Is Vertex AI better only for big production-level projects? : Not necessarily. While Vertex AI is often associated with large-scale production deployments, it’s not just for big projects. It’s useful for any project where you want to streamline and automate parts of the ML lifecycle, including small-to-medium-size projects. It offers tools that can save time, even for personal or academic projects, such as:
- Model management: Easily track different model versions, perform A/B testing, etc.
- AutoML: If you're still learning, you can take advantage of Google’s pre-built models or automated machine learning pipelines.
- Experimentation: The built-in feature for running experiments and comparing different model versions can be useful even for smaller projects.
- That being said, if you’re working on a simple, single-project ML model and just need to deploy it quickly, a custom solution might be easier and cheaper.
3. For a student building small projects, what would you suggest? : As a student, it’s good to experiment with both approaches so you understand the trade-offs. Here's what I'd suggest:
- Start with Cloud Run: Since you already have experience with Flask on Cloud Run, I’d recommend building more small, quick prototypes using Cloud Run or even App Engine. It's a good way to learn about managing your own infrastructure and dealing with more manual aspects of deployment.
- Experiment with Vertex AI: Once you’re comfortable with the basics, try deploying on Vertex AI to understand the platform’s higher-level abstractions. You can start with Vertex AI for free within the Google Cloud free tier and explore things like model deployment, endpoint management, monitoring, and scaling.
- Cost consideration: If you're running small models or personal projects, deploying via Vertex AI might be overkill in terms of complexity and cost. However, it’s worth experimenting for learning purposes.
Was this helpful? If so, please accept this answer as “Solution”. If you need additional assistance, reply here within 2 business days and I’ll be happy to help.
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