Maximize your accelerator utilization for reduced training time to minimize costs and improve metrics across the board


ML pipelines are important because you can’t train (and deploy) your model just once. Automation is essential for every production-grade model.

Back to the past


Automate the workflow to build, validate, and deploy machine learning models.

What is TensorFlow Extended (TFX)?


How to train and deploy a serverless Sentiment Analysis model and API to Google Cloud


Exports


We strive to bring people together to work on AI solutions.

  • Text Classification
  • Entity Annotation
  • Sequence to Sequence Annotation
  • Sentiment Annotation
  • Image Classification Annotation


  • Finished the core product features
  • Bought our .com domain
  • Put together ioannotator.com
  • Added metrics
  • Made the platform multi-user ready
  • Added additional Natural Language annotation tools
  • Release webhooks for better integration into machine learning pipelines
  • Migrated the app to app.ioannotator.com
  • Released support for…


A personal approach to machine learning 👩🏻‍🚀 👨🏻‍🚀 🚀

Photo by Tim Mossholder on Unsplash


Kubeflow is a large ecosystem, a stack of different open source tools ML tools.

Photo by Ryan Quintal on Unsplash

Goals

  • Demonstrate how to build pipelines.
  • Demonstrate how to create components.
  • Demonstrate how to use components.
  • Demonstrate how to run pipelines and experiments inside of a Notebook.
  • Easy to understand and ready to use examples.

Pipelines

Component


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Sascha Heyer

Hi, I am Sascha, Senior Machine Learning Engineer at @doitint and Founder of IOAnnotator an AI Platform.

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