Live Webinar: Learn how to build better machine learning pipelines
In this webinar, Jan 15 @ 12PM EST, we'll offer solutions to the common challenges data scientists and data engineers face when building a machine learning pipeline. Register now to attend live or to watch a recording afterwards.
Streamlining your machine learning pipeline is critical for enterprise data science to deliver better business results. Accelerating the process from data, to processing to training to deployment and back again will help you get better performing models, faster. In this webinar we'll offer solutions to the common challenges data scientists and data engineers face when building a machine learning pipeline. We will dissect each part of the pipeline and offer strategies on how to design your machine learning pipelines for a more efficient, integrated and automated process. We'll tackle ways to connect all your data sourcing in one unified location. How to create modular ML components for easy reproducibility, and automate MLOps for quick training of models and hyperparameter optimization. Streamline frequent deployment of models leveraging the power of Kubernetes. And lastly, you'll learn to design a monitoring toolkit with Grafana and Kibana for easy CI/CD. Join Solutions Architect, Aaron Schneider as he builds an end-to-end machine learning pipeline, and explains how to optimize each section for a more efficient workflow.
Key webinar takeaways:
Set up an efficient machine learning pipeline
Learn key MLOps solutions to streamline science and engineering
Create reusable ML components
Build a suite of monitoring and visualization tools
Instantly train and deploy ML models with Kubernetes
Use CI/CD to design an auto-adaptive machine learning pipeline
Unable to attend?
Register for a recording of the webinar and copy of the presentation following the live stream.