Implementing Machine Learning Workflows on Nomad

As machine learning becomes more widespread across industries, the need to enable teams to quickly and efficiently train, evaluate and serve models becomes important for a successful ML project. In this talk I'll explain how we're using HashiCorp and Jupyter Notebooks to handle the machine learning lifecycle. I'll give an overview on a basic setup we established to manage, train and serve ML Applications and challenges we're facing moving forward to a Nomad driven MLOps platform. Finally, I demo our self-service approach for data scientist to spin up isolated Jupyter Notebooks on a nomad cluster via Jupyter Hub for their experiments. By the end of this talk, you will learn how a ML workflow can be implemented with Nomad and give developers the ability to train models in a self-served manner. Knowledge of ML is not required and all ML concept that are relevant to the talk will be introduced. While the talk will use NLP as an example, the processes described will largely be generic and adaptable to other types of machine learning models.

As machine learning becomes more widespread across industries, the need to enable teams to quickly and efficiently train, evaluate and serve models becomes important for a successful ML project.

In this talk I'll explain how we're using HashiCorp and Jupyter Notebooks to handle the machine learning lifecycle.

I'll give an overview on a basic setup we established to manage, train and serve ML Applications and challenges we're facing moving forward to a Nomad driven MLOps platform.

Finally, I demo our self-service approach for data scientist to spin up isolated Jupyter Notebooks on a nomad cluster via Jupyter Hub for their experiments.

By the end of this talk, you will learn how a ML workflow can be implemented with Nomad and give developers the ability to train models in a self-served manner. Knowledge of ML is not required and all ML concept that are relevant to the talk will be introduced. While the talk will use NLP as an example, the processes described will largely be generic and adaptable to other types of machine learning models.