Natural Language Processing: AutoML vs handcrafted models

The progress that was recently made in large data handling technology and AI frameworks enable developers to create and deploy deep learning models with as little effort as clicking a few buttons on the screen. And yet, many models require handcrafting and customization.

In this session we will host two talks:

Gad Benram will be talking about using a UI or an API based on Tensorflow Estimators, models can be built and served without writing a single line of machine learning code.
He will demonstrate:

1. Use your existing SQL knowledge to build streaming pipelines
2. Submit hyper-parameter tuning jobs
3. Deploy a pipeline and serve results with a BigQuery
   See more details at this blog post:
   https://blog.doit-intl.com/codeless-ml-with-tensorflow-4683fea8e4a0

Adam Bali will be talking about how modeling natural language for industry solutions imposes extreme challenges.
Companies invest endless efforts in making sense of piles of textual human communication. Handling different tasks, domains and contexts requires extra attention and creative methodologies. This talk will outline some of those challenges, and demonstrate how approaches like semi-supervised and transfer learning can be utilized In the pipeline of our product, designed to detect sensitive information across any organization's data silos. Applying those enabled us to craft robust solutions, even when labeled data is lacking.