March 5, 2019 | by Diane Mueller
In this OpenShift Commons Briefing, DataBricks' Mani Parkhe gave an excellant introduction to MLFlow, an open source platform to manage the Machine Learning lifecycle, including experimentation, reproducibility and deployment.
MLFlow currently has three components:
Following the introduction, Red Hat's Zak Hassan gave an demonstration of the MLFlow operator for deploying MLFlow on Kubernetes. Building machine learning systems is not just a one-off effort; the process is iterative, and therefore, managing the lifecycle of the machine learning algorithms and applications is a key factor for success. Naturally, an operator for MLFlow to helps it better integrate into a Kubernetes environment.
Databricks MLFlow Intro Slides from the briefing: MLflow at OpenShift Commons ML SIG meeting Feb 2019
Red Hat's Zak Hassan's MLFLow Operator Slides from the briefing: OpenShift Commons ML Sig - Experiment Tracking On OpenShift With MLFLow
Check out the upcoming SIG meeting dates on the calendar here: https://commons.openshift.org/events.html
The OpenShift Commons Gathering brings together experts from all over the world to discuss the container technologies, operators, the operator framework, best practices for cloud-native application developers and the open source software projects that underpin the OpenShift ecosystem to help take us all to the next level in cloud-native computing. This next gathering will feature 400+ developers, project leads, cloud architects, DevOps professionals, sysadmins, and cloud-native practitioners coming together to explore the next steps in making container technologies successful and secure at scale.
Confirmed Keynotes and Speakers from Red Hat already include:
More speakers and panelists are being added, check out the agenda for updates. Please note: Pre-registration is required. Click here to register.
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