Presentation: Practical NLP for the Real World
Share this on:
This presentation is now available to view on InfoQ.com
Watch video with transcriptAbstract
Most companies have an abundance of text data they could leverage information from. Whether you'd like to classify incoming support ticket requests, detect when a candidate is available for an interview or suggest useful replies to emails, leveraging NLP techniques can often improve your products and allow you to build entirely new ones.
The field of NLP is often not the most approachable, however. The field ranges from linguistics to cutting edge deep learning, so it can be hard to find tools to build a practical product in a reasonable timeframe.
In this talk, we will cover concrete examples of how to build practical applications using NLP. In the real world, most gains come from improvements to the pipeline, not necessarily the model. For this reason, we will dive into data visualization and labelling, as well as model validation.
We will walk through code, plots, and leave ample room for practical questions.
2019 Tracks
-
Hands-on Codelabs & Speakers Office Hours
Codelabs are a self-guided tutorial of a product, API, or tool kit followed by an Office Hour period with the lab’s creator.
-
Solving Software Engineering Problems with Machine Learning
Interesting machine learning use cases changing how we develop software today, including planned topics touching on infrastructure optimization, developer experience, security, and more.
-
Predictive Architectures in the Real World
Case Study focused look at end to end predictive pipelines from places like Salesforce, Uber, Linkedin, & Netflix.
-
Predictive Data Pipelines & Architectures
Case Study focused look at end to end predictive pipelines from places like Salesforce, Uber, Linkedin, & Netflix
-
Sequential Data: Natural Language, Time Series, and Sound
Techniques, practices, and approaches around time series and sequential data. Expect topics including image recognition, NLP/NLU, preprocess, & crunching of related algorithms.
-
ML in Action
Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud
-
Deep Learning in Practice
Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.
-
Handling Sequential Data Like an Expert / ML Applied to Operations
Discussing the complexities of time (half track) and Machine Learning in the data center (half track). Exploring topics from hyper loglog to predictive auto-scaling in each of two half-day tracks.
Half-day tracks -
AI Meets the Physical World
Where AI touches the physical world, think drones, ROS, NVidia, TPU and more.