dataset

Spark NLP in action: How Indeed applies NLP to standardize rsum content at scale

Spark NLP in action: How Indeed applies NLP to standardize rsum content at scale

January 6, 2020

Alexander Thomas and Alexis Yelton demonstrate how to use Spark NLP and Apache Spark to standardize semistructured text, illustrated by Indeed's standardization process for rsum content.
Lightning Talk: Automated AI Model Deployment within ONAP

Lightning Talk: Automated AI Model Deployment within ONAP

January 5, 2020

AI will enable more intelligent automation, able to constantly adapt to field reality and specificities.Through this talk, a prospective vision of the management of an Al model learning, deployment an …
The unreasonable effectiveness of transfer learning on NLP

The unreasonable effectiveness of transfer learning on NLP

January 5, 2020

Transfer learning has been proven to be a tremendous success in computer visiona result of the ImageNet competition. In the past few months, there have been several breakthroughs in natural language processing with transfer learning, namely ELMo, OpenAI Transformer, and ULMFit. David Low demonstrates how to use transfer learning on an NLP application with SOTA accuracy.
The vegan data diet: How Wikipedia cuts down privacy issues while keeping data fit

The vegan data diet: How Wikipedia cuts down privacy issues while keeping data fit

January 4, 2020

Analysts and researchers studying Wikipedia are hungry for long-term data to build experiments and feed data-driven decisions. But Wikipedia has a strict privacy policy that prevents storing privacy-sensitive data over 90 days. Marcel Ruiz Forns explains how the Wikimedia Foundation's analytics team is working on a vegan data diet to satisfy both.
An artificial intelligence framework to counter international human trafficking

An artificial intelligence framework to counter international human trafficking

January 3, 2020

Sources of international human trafficking data contain a wealth of textual information that is laborious to assess using manual methods. Tom Sabo demonstrates text-based machine learning, rule-based text extraction to generate training data for modeling efforts, and interactive visualization to improve international trafficking response.
Beyond Word2Vec: Using embeddings to chart out the ebb and flow of tech skills

Beyond Word2Vec: Using embeddings to chart out the ebb and flow of tech skills

January 2, 2020

Word embeddings such as word2vec have revolutionized language modeling. Maryam Jahanshahi discusses exponential family embeddings, which apply probabilistic embedding models to other data types. Join in to learn how TapRecruit implemented a dynamic embedding model to understand how tech skill sets have changed over three years.
Executive Briefing: The hidden data in AI IP

Executive Briefing: The hidden data in AI IP

December 31, 2019

Three elements will control the AI market: technology, data, and IP rights. Leveraging rich patent data, Thomas Marlow uncovers the companies with the top patent holdings across the world in groundbreaking research and implementation technologies, surfacing insights into the sources and owners of AI technology as well as the hurdles and opportunities that those entering the field today face.
Industrialized capsule networks for text analytics

Industrialized capsule networks for text analytics

December 30, 2019

Vijay Agneeswaran and Abhishek Kumar offer an overview of capsule networks and explain how they help in handling spatial relationships between objects in an image. They also show how to apply them to text analytics. Vijay and Abhishek then explore an implementation of a recurrent capsule network and benchmark the RCN with capsule networks with dynamic routing on text analytics tasks.
Leveraging AI for social good

Leveraging AI for social good

December 30, 2019

The hardware, software, and algorithms that automatically tag our images or recommend the next book to read can also improve medical diagnosis and protect our natural resources. Jack Dashwood and Anna Bethke discuss a variety of technical projects at Intel that have enabled social good organizations and provide guidance on creating or engaging in these types of projects.
Manipulating and measuring model interpretability

Manipulating and measuring model interpretability

December 29, 2019

Forough Poursabzi-Sangdeh argues that to understand interpretability, we need to bring humans in the loop and run human-subject experiments. She describes a set of controlled user experiments in which researchers manipulated various design factors in models that are commonly thought to make them more or less interpretable and measured their influence on users behavior.
Open source tools for machine learning model and dataset versioning

Open source tools for machine learning model and dataset versioning

December 29, 2019

ML model and dataset versioning is an essential first step in the direction of establishing a good process. Dmitry Petrov and Ivan Shcheklein explore open source tools for ML models and datasets versioning, from traditional Git to tools like Git-LFS and Git-annex and the ML project-specific tool Data Version Control or DVC.org.
Using AI to transform high-volume, confidential, disparate data for the United States Patent Office

Using AI to transform high-volume, confidential, disparate data for the United States Patent Office

December 28, 2019

Tammy Bilitzky shares a case study that details lights-out automation and explains how DCL uses AI to transform massive volumes of confidential disparate data into searchable and structured information. Along the way, she outlines considerations for architecting a solution that processes a continuous flow of 5M+ pages of complex work units.
Applied machine learning in finance

Applied machine learning in finance

December 27, 2019

Quantitative finance is a rich field in finance where advanced mathematical and statistical techniques are employed by both sell-side and buy-side institutions. Chakri Cherukuri explains how machine learning and deep learning techniques are being used in quantitative finance and details how these models work under the hood.
Building high-performance text classifiers on a limited labeling budget

Building high-performance text classifiers on a limited labeling budget

December 26, 2019

Robert Horton, Mario Inchiosa, and Ali Zaidi demonstrate how to use three cutting-edge machine learning techniquestransfer learning from pretrained language models, active learning to make more effective use of a limited labeling budget, and hyperparameter tuning to maximize model performanceto up your modeling game.
Data processing at the speed of 100 Gbps using Apache Crail

Data processing at the speed of 100 Gbps using Apache Crail

December 26, 2019

Modern networking and storage technologies like RDMA or NVMe are finding their way into the data center. Patrick Stuedi offers an overview of Apache Crail (incubating), a new project that facilitates running data processing workloads (ML, SQL, etc.) on such hardware. Patrick explains what Crail does and how it benefits workloads based on TensorFlow or Spark.
Faster ML over joins of tables

Faster ML over joins of tables

December 24, 2019

Arun Kumar details recent techniques to accelerate ML over data that is the output of joins of multiple tables. Using ideas from query optimization and learning theory, Arun demonstrates how to avoid joins before ML to reduce runtimes and memory and storage footprints. Along the way, he explores open source software prototypes and sample ML code in both R and Python.
ROCKSET: The design and implementation of a data system for low-latency queries for search and analytics

ROCKSET: The design and implementation of a data system for low-latency queries for search and analytics

December 22, 2019

Most existing big data systems prefer sequential scans for processing queries. Igor Canadi and Dhruba Borthakur challenge this view, offering an overview of converged indexing: a single system called ROCKSET that builds inverted, columnar, and document indices. Converged indexing is economically feasible due to the elasticity of cloud-resources and write optimized storage engines.
Scaling visualization for big data and analytics in the cloud

Scaling visualization for big data and analytics in the cloud

December 21, 2019

Jaipaul Agonus and Daniel Monteiro do Carmo Rosa detail big data analytics and visualization practices and tools used by FINRA to support machine learning and other surveillance activities that the Market Regulation Department conducts in the AWS cloud.
User-based real-time product recommendations leveraging deep learning using Analytics Zoo on Apache Spark and BigDL

User-based real-time product recommendations leveraging deep learning using Analytics Zoo on Apache Spark and BigDL

December 20, 2019

User-based real-time recommendation systems have become an important topic in ecommerce. Lu Wang, Nicole Kong, Guoqiong Song, and Maneesha Bhalla demonstrate how to build deep learning algorithms using Analytics Zoo with BigDL on Apache Spark and create an end-to-end system to serve real-time product recommendations.
Building a robust content recommendation platform for 60 million news readers

Building a robust content recommendation platform for 60 million news readers

December 18, 2019

Matt Chapman leads a walkthrough of the architecture and open source components that serve Tribune Publishing's content recommendation system, powered by online machine learning at scale. Find out how multiple publications, multiple recommendation algorithms, and one scalable architecture regularly achieve double the performance of the legacy solution.
Measuring and Optimizing Kubeflow Clusters at Lyft

Measuring and Optimizing Kubeflow Clusters at Lyft

December 13, 2019

Machine learning workloads are often resource-intensive operations. As companies adopt more of these workloads, tracking resource consumption and optimizing spending becomes more challenging.At Lyft, …
Serving HTC Users in Kubernetes by Leveraging HTCondor

Serving HTC Users in Kubernetes by Leveraging HTCondor

November 22, 2019

High Throughput Computing (HTC), sometimes also called batch computing, has long been and still is the major workhorse for most R&D organizations. Typical workloads include parameter sweeps, Monte Car …