MLflow Control Plane: Govern LLM Traffic in AI Gateway | MLflow A–Z: C
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Author: MLflow · Watch on YouTube
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Original language: English
Original video: MLflow Control Plane: Govern LLM Traffic in AI Gateway | MLflow A–Z: C · Channel: MLflow · 01:02 · Watch on YouTube
Topics: AI agents, AI governance and regulation, AI infrastructure and hardware
Summary
Section titled “Summary”The video introduces MLflow’s AI Gateway as a control plane for managing GenAI applications as they move into production. It describes the challenges of handling access control, budgets, and guardrails independently. The gateway centralizes these controls and supports model routing and usage tracking. Viewers are directed to a separate MLflow video for setup instructions.
Key points
Section titled “Key points”- Deploying multiple agents and agentic applications to production calls for a control plane.
- Production systems may need access control, budget policies, and guardrails.
- Managing control points independently can be challenging.
- MLflow’s AI Gateway provides centralized management for these controls.
- Guardrail instructions can be added on top of a model.
- Budget policies can be set for teams.
- Requests can be routed to multiple models based on traffic.
- Usage can be tracked in a single dashboard.
- A detailed AI Gateway setup video is available on the MLflow channel.
Chapters
Section titled “Chapters”| Start | Chapter | Summary |
|---|---|---|
| 00:00 | Why production AI needs a control plane | The video introduces the challenge of teams deploying agents and explains the need for a control plane. |
| 00:30 | Centralized controls with the AI Gateway | The video describes how the gateway centralizes guardrails, budget policies, model routing, and usage tracking. |
| 01:02 | Setup resource and closing | The speaker points viewers to a detailed setup video and invites them to request its link. |
Quotes
Section titled “Quotes”You need a control plane. — 00:00
Managing all these control points independently is quite challenging. — 00:30
we’ll be able to manage all these things in a centralized way. — 00:30
we have done a detailed video in the MLflow channel. — 01:02
Concepts and entities
Section titled “Concepts and entities”control plane, AI Gateway, MLflow, guardrail, budget policies
Full transcript (original language)
Section titled “Full transcript (original language)”00:00 What happens when every team of yours start deploying their own agents? When all these agentic applications moves to production, you need more than LLMs. You need a control plane. And how can you achieve it? Hello everyone. Welcome to MLflow A to Z series. 26 days we are going to discuss about 26 amazing MLflow features. Today we are at day three with letter C where we are going to talk about control plane. If you are deploying GenAI based model in production, there are multiple different control points. Be it access control,
00:30 adding budget policies, or setting up a guardrail. Managing all these control points independently is quite challenging. With MLflow’s AI gateway, we’ll be able to manage all these things in a centralized way. You can add guardrail instructions on top of your model. You can set up budget policies for your own team. You can route your request to multiple models based on the traffic. You can track all your usage in a single dashboard and multiple other things. So don’t wait and go ahead and integrate MLflow’s AI gateway to your pipeline and manage
01:02 everything in a centralized way. If you want to know how to set up MLflow AI gateway, we have done a detailed video in the MLflow channel. Type control in the comment section. We will share you the link to the video. Thank you very much and see you soon in the next video.