On Demand Webinar: Build machine learning pipelines

Download the Presentation On-demand

Streamline your machine learning pipeline

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 and end-to-end machine learning pipeline, and explains how to optimize each part for a more efficient workflow. 

Key webinar takeaways: 

  • Set up an efficient machine learning pipeline
  • Learn key MLOps solutions streamlining 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

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 and end-to-end machine learning pipeline, and explains how to optimize each part for a more efficient workflow. 

Key webinar takeaways: 

  • Set up an efficient machine learning pipeline
  • Learn key MLOps solutions streamlining 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
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