Join our expert consultation call and see how ZenML can enhance your machine learning workflow. We focus on your unique needs to offer tailored, practical solutions.
Step 1
Understand and Analyze
We start by getting to know you and your team. What are your ML ops challenges? What's your current stack? Let’s dive deep into your unique context.
Step 2
Interactive ZenML Demo
Witness firsthand the power and simplicity of ZenML. We’ll walk you through our intuitive dashboard, showcasing how it effortlessly integrates with your existing workflow.
Step 3
Tailored Solutions for You
Your project is unique, and so should be your solution. We’ll discuss how ZenML caters specifically to your projects, whether it’s managing data, version tracking, or cloud operations.
Step 4
Plan Your Success Path
Before we part, we’ll outline the next steps. Start your free ZenML trial and join our community for ongoing support. We’re here to ensure your smooth transition to a more efficient ML ops experience.
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"ZenML offers the capability to build end-to-end ML workflows that seamlessly integrate with various components of the ML stack, such as different providers, data stores, and orchestrators.".
Harold Giménez
SVP R&D at HashiCorp
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What is the difference between ZenML and other machine learning orchestrators?
Unlike other machine learning pipeline frameworks, ZenML does not take an opinion on the orchestration layer. You start writing locally, and then deploy your pipeline on an orchestrator defined in your MLOps stack. ZenML supports many orchestrators natively, and can be easily extended to other orchestrators. Read more about why you might want to write your machine learning pipelines in a platform agnostic way here.
Does ZenML integrate with my MLOps stack (cloud, ML libraries, other tools etc.)?
As long as you're working in Python, you can leverage the entire ecosystem. In terms of machine learning infrastructure, ZenML pipelines can already be deployed on Kubernetes, AWS Sagemaker, GCP Vertex AI, Kubeflow, Apache Airflow and many more. Artifact, secrets, and container storage is also supported for all major cloud providers.
Does ZenML help in GenAI / LLMOps use-cases?
Yes! ZenML is fully compatabile, and is intended to be used to productionalize LLM applications. There are examples on the ZenML projects repository that showcases our integrations with Llama Index, OpenAI, and Langchain. Check them out here!
How can I build my MLOps/LLMOps platform using ZenML?
The best way is to start simple. The starter and production guides walk you through how to build a miminal cloud MLOps stack. You can then extend with the other numerous components such as experiment tracker, model deployers, model registries and more!
What is the difference between the open source and cloud product?
ZenML is and always will be open-source at its heart. The core framework is freely available on Github and you can run and manage it in-house without using the cloud product. On the other hand, the cloud product offers one of the best experiences to use ZenML, and includes a managed version of the OSS product, and some features that create the best collaborative experience for many companies that are scaling their ML efforts. You can see a more detailed comparison here.
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