Learn How To Build Machine Learning Models Faster at Bristol MLOps

Tune in to Bristol MLOps and learn the easiest path to reproducible machine learning with Kubeflow + Rok + Kale

Session: The easiest path to reproducible ML – Kubeflow + Rok + Kale


British Summer Time (BST / GMT+1)

September 24th – 11:40 – 12:10

US Eastern Daylight Time (EDT / GMT-4)

September 24th – 06:40 – 07:10

US Pacific Daylight Time (PDT / GMT-7)

September 24th – 03:40 – 04:10

How To Attend

Get your tickets here

Session Details

Kubeflow is an open source toolkit for Machine Learning on Kubernetes, designed to make deployments of Machine Learning workflows on Kubernetes simple, portable, and scalable. It is an exponentially growing project and very popular among data scientists, with outstanding community and industry support.

Using Kubeflow makes it easier for Data Scientists to automate and operationalize common Machine Learning workflows, like distributed training, hyperparameter tuning, running complex data pipelines, logging and storing metadata and artifacts, as well as working in shared JupyterLab environments. Kubeflow strives to provide all the bells and whistles for a comprehensive ML environment, but given the inherent complexity of running Machine Learning workflows at scale, Kubeflow remains more suited to Software/ML Engineers that possess a fair understanding of Kubernetes concepts, specific SDKs, and practices.

In this talk, we will lower that complexity bar, taking Kubeflow’s MLOps paradigm to the next level, empowering Data Scientists to leverage on the Ops while focusing just on the ML. Easy Notebook deployments, reproducibility and collaboration are the key aspects to nail down for a seamless experience. Kale is the conversion engine that provides an integrated JupyterLab experience to deploy pipelines to Kubeflow. Rok is the storage and data management technology that takes care of making all your workflows portable, versioned, and reproducible. When put together, they allow for a dramatic improvement in the time and effort for scaling up Machine Learning workloads on-prem or in the Cloud!

Caution: This talk’s contents are highly addictive, any extended use might cause distress, pain or anxiety towards other less innovative, disruptive and cumbersome technologies. Come and listen at your own risk.


Stefano Fioravanzo

Stefano Fioravanzo

Software Engineer

Stefano Fioravanzo is a Software Engineer at Arrikto, his background is in Data Science and ML Research. He understands the value of building robust Machine Learning infrastructure and providing Data Scientist with the necessary tools to scale up their workflows. He works as a full-time contributor to Kubeflow and he is the creator of the Kubeflow Kale project which enables Jupyter Notebooks deployments to Kubeflow Pipelines.

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