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
- laptop - Linux or MacOS (highly recommended) or Windows (optional)
- Git (or Git for Windows if using Windows, which includes Git Bash)
- Docker Desktop
- For Windows, please make sure that you have admin permissions and can share your drive with Docker, and open ports 445, 8080, 8888, and 6006.
- Min 3GB available disk space
- Min 8GB DRAM
- **BEFORE** the workshop, please run: docker pull gcr.io/tfx-oss-public/tfx-workshop:latest
- **BEFORE** the workshop, also please run: 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 and TensorFlow Developer Advocate at Google with a passion for helping developers quickly learn what they need to be productive. 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 deployed production ML applications and 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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