ML in production: Getting started with TensorFlow Extended (TFX)
Who is this presentation for?
- Data scientists, ML engineers, researchers, and ML ops and DevOps practitioners
Putting together an ML production pipeline for training, deploying, and maintaining ML and deep learning applications is much more than just training a model. Google has taken years of experience in developing production ML pipelines and offered the open source community TensorFlow Extended (TFX), an open source version of tools and libraries that Google uses internally.
Robert Crowe outlines what’s involved in creating a production ML pipeline and walks you through working code.
- Experience with machine learning, Python, and Linux
Materials or downloads needed in advance
- A Linux or MacOS (highly recommended) or Windows (optional) laptop with at least 3GB available disk space and 8GB DRAM Git (or Git for Windows if using Windows, which includes Git Bash) and Docker Desktop installed
- For Windows, please make sure that you have admin permissions, can share your drive with Docker, and open ports 445, 8080, 8888, and 6006
- Before the workshop, run: docker pull gcr.io/tfx-oss-public/tfx-workshop:latest and docker pull tensorflow/serving:nightly
What you'll learn
- Understand the issues and best practices for putting machine learning models and applications into production
Robert Crowe is a data scientist at Google with a passion for helping developers quickly learn what they need to be productive. A TensorFlow addict, he’s used TensorFlow since the very early days and is excited about how it’s evolving quickly to become even better than it already is. Previously, Robert led software engineering teams for large and small companies, always focusing on clean, elegant solutions to well-defined needs. In his spare time, Robert sails, surfs occasionally, and raises a family.
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