One-Click Solution

Why This Matters

The Open Source LLMOps Platform We Always Needed

Let’s be honest, as engineers in the AI/ML space, we have all been there. You have a vision, a groundbreaking application you want to build. But the path to getting there is paved with complex infrastructure. Do you try to integrate existing solutions? Do you invest heavily in proprietary platforms that might lock you in?

About 18 months ago, the TRI^labs team at Ardent faced this exact dilemma. We were tasked with pushing the boundaries of AI/ML, but our initial exploration revealed a significant gap. While individual open-source tools offered incredible potential, the lack of seamless integration for our desired end-to-end workflows was a major roadblock. Furthermore, the need to operate across diverse environments and multiple cloud providers create an even bigger challenge.

What happens when your team needs to build something truly unique, and the very platform to build it on doesn’t exist? And what if the need to replicate that platform arises again and again, for different projects, different teams?

The answer, for us, was to build. To create a tool that automates the processes, that unify the different components. We leveraged the power and flexibility of Kubernetes to create a platform-agnostic foundation, capable of running anywhere we needed it to any cloud environment.

But our vision extended beyond our internal needs. We recognized that the challenges we faced were likely shared by the broader engineering community. The desire for a streamlined, scalable, and open approach to LLMOps and MLOps on Kubernetes is universal.

This realization led to the development of AiStreamliner. It’s more than just a collection of integrated tools; it’s a one-click deployment solution designed to empower engineering teams to rapidly provision the environments they need, freeing them to focus on innovation rather than infrastructure headaches.

By open-sourcing AiStreamliner, we aim to contribute to the community. We believe in the power of collaboration and the potential of open source to drive innovation. We invite you to join us on this journey, to explore AiStreamliner, contribute your expertise, and help us build the future of scalable AI together.

The Challenge

Common Challenges for ML & LLM

Deploying Machine Learning (ML) and Large Language Models (LLMs) at scale isn’t always smooth sailing.

These issues slow down development, increase costs, and make it harder to get AI innovations into the real world.

Engineers often encounter these key challenges:
Tool Fragmentation
 The ML lifecycle uses many disconnected tools, creating complex and inefficient workflows.
LLM Complexity
 Managing resource-intensive LLMs alongside traditional ML models significantly increases operational burden.
Scalability Limits
 Scaling deployments can be difficult due to infrastructure limitations and the high cost of resources.
Integration Gaps
 A lack of unified, platform-agnostic tools makes it hard to evaluate performance across different environments.

We saw these challenges firsthand, and that’s why we’re building a better way.

Our Solution

Streamlined AI Operations with AiStreamliner

We are solving the complexities of AI deployment with AiStreamliner.

AiStreamliner is a comprehensive open-source LLMOps and MLOps platform built for scalable Kubernetes deployments.

Our solution provides:
End-to-End Workflow Integration
 We combine the entire ML lifecycle, from data management and experimentation to deployment and monitoring, creating seamless workflows for both traditional ML and LLMs.
Kubernetes-Powered Scalability
 Built on Kubernetes, AiStreamliner offers efficient and flexible scaling to meet your evolving infrastructure needs.
Platform Agnostic Design
 Deploy anywhere – across various cloud providers or on-premises – eliminating vendor lock-in and maximizing flexibility.
Accelerated Innovation
 Streamlined experimentation empowers your teams to test ideas faster and iterate more efficiently.
Enhanced Productivity
 End-to-end automation reduces operational overhead, freeing your data scientists to focus on what matters most: building better models.
Complete Open-Source
 Gain full control and flexibility with a 100% open-source platform, supported by a transparent, community-driven development model.

Platform Architecture

A Layered Architecture for Performance and Flexibility

AiStreamliner is built on a robust four-layer architecture, designed for ease of use and extensibility.

Unified User Interface
 We have enhanced the familiar Kubeflow dashboard to provide a central and intuitive UI for managing your entire ML/LLM lifecycle.
Core Services & APIs
 This layer houses our custom-built core components and APIs, providing the intelligence and functionality that power AiStreamliner’s integrated workflows.
Kubernetes Orchestration
 Kubernetes forms the foundation for deployment, scaling, and efficient resource management across your chosen infrastructure.
Persistent Storage
 We leverage Kubernetes Persistent Volumes to ensure reliable and scalable storage for your critical data and models.

Key Open-Source Components We Leverage:

Kubeflow
 For robust workflow orchestration and scalable training.
MLflow
 To streamline experiment tracking and manage your model registry.
KServe
 Enabling efficient and scalable model serving and inference.
LakeFS
 Providing Git-like data version control for data management and reproducibility.
AIM
 For detailed tracking and visualization of deep learning experiments.

 

Contact Us

Senior Director of Solutions Architecture

Nate Lebel

nathan.lebel@ardentmc.com

Chief Technology Officer

Mireille Estephan

mireille.estephan@ardentmc.com

@TRI_Labs_Ardent
 

@TRI^labs_Ardent

We’d love to hear from you! Please get in touch with our team.

  • Have questions about AiStreamliner?
  • Want to contribute to the project?
  • Interested in partnership opportunities?
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