Kubeflow
- By Kubeflow Charmers | bundle
- Cloud
| Channel | Revision | Published |
|---|---|---|
| latest/stable | 414 | 01 Dec 2023 |
| latest/candidate | 294 | 24 Jan 2022 |
| latest/beta | 430 | 30 Aug 2024 |
| latest/edge | 423 | 26 Jul 2024 |
| 1.9/stable | 426 | 31 Jul 2024 |
| 1.9/beta | 420 | 19 Jul 2024 |
| 1.9/edge | 425 | 31 Jul 2024 |
| 1.8/stable | 414 | 22 Nov 2023 |
| 1.8/beta | 411 | 22 Nov 2023 |
| 1.8/edge | 413 | 22 Nov 2023 |
| 1.7/stable | 409 | 27 Oct 2023 |
| 1.7/beta | 408 | 27 Oct 2023 |
| 1.7/edge | 407 | 27 Oct 2023 |
| 1.6/stable | 329 | 07 Sep 2022 |
| 1.6/beta | 326 | 23 Aug 2022 |
| 1.6/edge | 328 | 07 Sep 2022 |
| 1.4/stable | 321 | 30 Jun 2022 |
| 1.4/edge | 320 | 30 Jun 2022 |
juju deploy kubeflow --channel beta
Deploy Kubernetes operators easily with Juju, the Universal Operator Lifecycle Manager. Need a Kubernetes cluster? Install MicroK8s to create a full CNCF-certified Kubernetes system in under 60 seconds.
Platform:
See also: How to build an MLOps pipeline with MLFlow, Seldon Core and Kubeflow
See more: List of MLOps tools
An MLOps pipeline is a set of steps that automates the process of creating and maintaining AI/ML models. In other words, Data Scientists create multiple notebooks while building their experiments, and naturally the next step is a transition from experiments to production-ready code. The best way to do this is to build an effective MLOps pipeline.
The MLOps process is basically divided into two main phases – experimentation and realisation. During experiments, data scientists focus on generating lots of ideas and validating them. On the other hand in the second realisation phase, we select a subset of the most promising ideas and deliver them to production. And for sure, only when they are a part of the business operations can they deliver the expected value.
An MLOps pipeline consists of the following four steps:
- Download the data
- Preprocess the data
- Train the model
- Deploy the model