See how the field of ML Ops is evolving and how HashiCorp Nomad is a great tool for scheduling and deployment of resources in a machine learning pipeline on hybrid infrastructure resources.
Google estimates only 1% of data is being used effectively by organizations. Industry trends in data team spending indicate that organizations want to turn this around. However, to be effective in hybrid infrastructure environments (the reality for most large enterprises), tooling needs to be leveraged that doesn't complicate an already complicated process.
HashiCorp’s Nomad is a federated workload scheduler. Its orchestration capabilities include parameterization and dependency specifications, as well as plugin ability with popular DAG (Directed Acyclic Graph) tools like Apache Spark. This makes Nomad an excellent choice for building machine learning (ML) pipelines and ML Ops, especially in a hybrid infrastructure, when interoperability across on-premise and cloud environments is desired.
In this demo project, HashiCorp solutions engineer Josh Jordan will demonstrate how HashiCorp Nomad is an open source tool capable of bringing smiles to the faces of data scientists everywhere. He will explore Nomad in this role, and as a component of a fully automated and integrated ML Ops practice.
You'll see where Nomad can fit amidst the moving parts of a fully automated, E2E ML pipeline. To do this, we already have an example project to test: a pipeline for training & deploying ML model versions.
Read the companion blog post in addition to watching this demo webinar.
0:00 — What is machine learning operations (ML Ops)?
9:58 — Why Nomad is a good choice for ML Ops
13:21 — Demo: Using Nomad for deploying ML model versions in a pipeline
33:18 — A look at the emerging trends in ML Ops
36:27 — Live Q&A