MLOps in action: Operationalize your ML workflow using pipeline templates

Machine learning (ML) is one of the top priorities for businesses of all sizes today. However, it’s still hard to productionalize data science use cases, especially because the journey from experimentation lacks standardization.

One way to address this challenge is to embrace MLOps practices and adopt a template-driven approach with reusable ML pipelines code. From a collaboration between Datatonic and Google Cloud, the MLOps Turbo Template is a great example of how it's possible to templatize the end-to-end ML lifecycle to help Data Scientists and ML Engineers kickstart their ML project quickly and efficiently on Vertex AI.

In this webinar, we'll announce the new version of the open-source MLOps Turbo Template, which introduces several new features, including Kubeflow Pipelines (KFP) v2 support, iterative training, model evaluation, and more. We’ll also test the new template with a LIVE demo!

Tune into this session with Ivan Nardini, Sales Engineer, Smart Analytics at Google Cloud, and Felix Schaumann, Head of Machine Learning Engineering at Datatonic, where you’ll:
Gain an understanding of MLOps best practices
Learn how MLOps Turbo Template can help you put MLOps best practices into action
See a walkthrough demonstration of how MLOps Turbo Template can kickstart and operationalize your MLOps workflow

This content is ideal for Data Scientists, Machine Learning Engineers, and anyone else who is interested in learning more about MLOps and pipelines.

01:50 Why MLOps?
07:52 ML Pipelines as backbone of MLOps
11:24 MLOps on Vertex AI - scale MLOps with Vertex AI Pipelines
18:08 ML at scale with Turbo Templates
32:28 Turbo Templates + KFP + Vertex AI
33:42 Live demo
1:02:54 Key takeaways
1:05:17 Q&A

New release of the MLOps Turbo Template now available: https://datatonic.com/insights/datato...
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