Enterprise AF solution for text classification (using BERT)
Who is this presentation for?
- Machine learning engineers, data scientists, and tech leads
Today we have machine learning engineers, software engineers, and data scientists. The trend in deep learning is that models are getting so powerful that there’s little need to know about the details of the specific algorithm, and instead the models can be immediately applied to custom use cases. Leonardo Apolonio explains how this trend will turn a machine learning engineer’s job into a software engineer’s skill.
- What is BERT?
- Why is BERT important?
- Fine-tuning BERT using AG News Dataset
- Writing TensorFlow Serving Client
- Building Docker containers for TensorFlow-Serving and a TensorFlow-Serving client
- Building Docker containers and push containers to Docker Hub
- Creating a Kubernetes cluster and deploy containers to Kubernetes in Google Cloud
- Familiarity with Python
- A working knowledge of TensorFlow and Kubernetes
Materials or downloads needed in advance
- A laptop
- Google Cloud and Docker Hub accounts
What you'll learn
- Learn how to use TensorFlow, Kubernetes on Google Cloud, and BERT (pretrained models)
Leonardo Apolonio is a machine learning engineer at Clarabridge, where he solves natural language processing (NLP) tasks, like detecting emotion, call reason, and expressed effort in the customer experience domain. He has experience maintaining and improving NLP pipelines to extract entities and topics from over 30 million websites daily, using the latest NLP and deep learning techniques. Leonardo has also built scalable analytics techniques for anomaly detection using datasets with billions of events.
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