If you're already utilizing one of the major clouds for ML/AI, you might be wondering: Why do I need ZenML at all? Doesn’t it add to the complexity rather than taking away from it? This article answers this question we’ve heard repeatedly, with practical insights from working with hundreds of companies building MLOps platforms using ZenML.
The Evolution of Cloud-Based ML/AI
Take a look at companies running ML/AI in the cloud, and you'll spot many who've been at it for years, some for over a decade. These organizations have honed their cloud ML/AI practices over time.
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ZenML was born from this evolution. We recognized that while cloud providers excel at offering foundational services like scalable storage, compute, and robust security, ML teams need more. They need a dedicated layer that empowers ML engineers and data scientists to develop systems quickly and independently.
From Prototypes to Production at Scale
Hacking together a few ML/AI prototypes in the cloud or getting started with default cloud services isn't too challenging. But as your efforts grow, consider this: How easily could you build ten production-ready systems with your current setup? Or a hundred?
Eventually, organizations realize they need a more robust, productive environment to efficiently tackle a wider range of ML/AI use cases. It's similar to how DevOps teams adopt tools like DataDog for advanced observability, or how data teams turn to Snowflake for warehousing. These tools build on cloud foundations to offer maintenance-free, developer-friendly services.
ZenML on Cloud MLOps Platforms: Practical Benefits
ZenML provides a comprehensive platform that addresses the core needs of ML/AI projects:
- Development Productivity: Speed up development cycles.
- Data Access: Scale from simple CSV files to enterprise-grade data management.
- ML-Focused APIs: Consistent, friendly interfaces for common ML/AI tasks.
- Efficient Compute: Leverage cloud resources cost-effectively.
- Streamlined Deployment: Make model deployment a routine team task.
- Robust Operations: Tools for maintaining and monitoring production systems.
Here is a list of features that ZenML augments on top of existing MLOps solutions on all the cloud providers:
“I can’t let my data or compute leave my cloud account”
We get it – most teams can't send data or compute resources outside their cloud environment. That's why ZenML is designed to work within your existing setup:
- All storage and compute stays in your cloud account. No data leaves your premises.
- ZenML integrates with your existing security policies, like IAM and SSO.
- You get a top-tier ML/AI platform without moving resources or changing security protocols.
"Won't it be challenging to switch our existing workflows?"
We hear this concern often, but rest assured: transitioning to ZenML is designed to be smooth and low-risk. Here's why:
- Minimal Code Changes: ZenML uses simple Python decorators (
@stepand@pipeline) that can be easily integrated into your existing codebase. This means you can adopt ZenML incrementally, without a complete overhaul of your workflows. - Data and Compute Stay Put: As mentioned earlier, all your data and compute resources remain within your cloud account. There's no need to migrate or expose sensitive information.
- Gradual Adoption: You can start small by implementing ZenML in a single project or team, then scale up as you see the benefits.
- Reversibility: If for any reason you decide ZenML isn't the right fit, you can easily revert to your previous setup. ZenML doesn't lock you in.
- Comprehensive Documentation and Support: Our extensive documentation and hands-on support ensure your team has the resources they need for a successful transition.
Remember, the goal of ZenML is to enhance your existing cloud MLOps setup, not replace it entirely. By providing a unified, developer-friendly layer on top of your cloud infrastructure, ZenML aims to make your ML workflows more efficient and manageable without disrupting what already works.
Your Next Step in ML/AI Evolution
ZenML doesn't replace cloud MLOps platforms. It complements them. If the idea of building cloud-agnostic ML/AI systems using a developer-friendly platform, securely running in your cloud account, and leveraging optimal resources across clouds resonates with you, it's time to give ZenML a try.
Deployment is straightforward, requiring no deep cloud expertise. We provide hands-on onboarding to get your team productive quickly.
Don't just take our word for it. Here's what Dragos Ciupureanu, VP of Engineering, Koble, shared:
“With ZenML, we're no longer tied to a single cloud provider. The flexibility to switch backends between AWS and GCP has been a game-changer for our team."
Ready to take your ML/AI efforts to the next level? Start your ZenML journey today by booking a demo!
