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Oct 29, 2018 · post

Coming Soon: Federated Learning

Federated Learning is a technology that allows you to build machine learning systems when your datacenter can’t get direct access to model training data. The data remains in its original location, which helps to ensure privacy and reduces communication costs.

Privacy and reduced communication makes federated learning a great fit for smartphones and edge hardware, healthcare and other privacy-sensitive use cases, and industrial applications such as predictive maintenance.

What’s the Status Quo?

To train a machine learning model you usually need to move all the data to a single machine or, failing that, to a cluster of machines in a data center.

This can be difficult for two reasons.

First, there can be privacy barriers. A smartphone user may not want to share their baby photos with an application developer. A user of industrial equipment may not want to share sensor data with the manufacturer or a competitor. And healthcare providers are not totally free to share their patients’ data with drug companies.

Second, there are practical engineering challenges. A huge amount of valuable training data is created on hardware at the edges of slow and unreliable networks, such as smartphones, IoT devices, or equipment in far-flung industrial facilities such as mines and oil rigs. Communication with such devices can be slow and expensive.

A Breakthrough Innovation

In federated learning, a server coordinates a network of nodes, each of which has training data that it cannot or will not share directly. The nodes each train a model, and it is that model which they share with the server. The server never has direct access to the training data. By moving models instead, federated learning helps to ensure privacy and minimizes communication costs.

In moving the majority of the work to the edge, federated learning is part of the trend to move machine learning out of the data center, for reasons that include speed and cost. But in federated learning, the edge nodes create and improve the model (rather than merely applying it). In this sense, federated learning goes far beyond what people usually mean when they talk about edge AI.

In the Cloudera Fast Forward Labs report, we discuss use cases ranging from smartphones to web browsers to healthcare to corporate IT to video analytics — all situations where privacy and bandwidth create challenges for distributed machine learning.

Our working prototype, Turbofan Tycoon, focuses in particular on industrial predictive maintenance with IoT data, where the training data is a sensitive asset.

The report will be available to corporate subscribers to Cloudera Fast Forward Labs’s advising service from Tuesday, November 13. The prototype will be available to the public the same day.

And all are welcome to join us on Thursday, November 15 at 10AM PT for a live webinar on “Federated Learning: ML with Privacy on the Edge”. Mike Lee Williams of Cloudera Fast Forward Labs will be joined by Andrew Trask (founder of the open source federated learning project OpenMined), Eric Tramel (Senior Research Scientist of healthcare AI startup Owkin), and Virginia Smith (Assistant Professor in Electrical and Computer Engineering at Carnegie Mellon University).

Click here to watch the webinar!

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Latest posts

Jun 22, 2020 · post

How to Explain HuggingFace BERT for Question Answering NLP Models with TF 2.0

by Victor · Given a question and a passage, the task of Question Answering (QA) focuses on identifying the exact span within the passage that answers the question. Figure 1: In this sample, a BERTbase model gets the answer correct (Achaemenid Persia). Model gradients show that the token “subordinate ..” is impactful in the selection of an answer to the question “Macedonia was under the rule of which country?". This makes sense .. good for BERTbase.
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Jun 16, 2020 · notebook

Evaluating QA: Metrics, Predictions, and the Null Response →

by Melanie · A deep dive into computing QA predictions and when to tell BERT to zip it! In our last post, Building a QA System with BERT on Wikipedia, we used the HuggingFace framework to train BERT on the SQuAD2.0 dataset and built a simple QA system on top of the Wikipedia search engine. This time, we’ll look at how to assess the quality of a BERT-like model for Question Answering.
qa.fastforwardlabs.com
May 19, 2020 · notebook

Building a QA System with BERT on Wikipedia →

by Melanie · So you’ve decided to build a QA system. You want to start with something simple and general so you plan to make it open domain using Wikipedia as a corpus for answering questions. You want to use the best NLP that your compute resources allow (you’re lucky enough to have access to a GPU) so you’re going to focus on the big, flashy Transformer models that are all the rage these days.
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Apr 28, 2020 · notebook

Intro to Automated Question Answering →

by Melanie · Welcome to the first edition of the Cloudera Fast Forward blog on Natural Language Processing for Question Answering! Throughout this series, we’ll build a Question Answering (QA) system with off-the-shelf algorithms and libraries and blog about our process and what we find along the way. We hope to wind up with a beginning-to-end documentary that provides:
qa.fastforwardlabs.com
Apr 1, 2020 · newsletter

Enterprise Grade ML

by Shioulin · At Cloudera Fast Forward, one of the mechanisms we use to tightly couple machine learning research with application is through application development projects for both internal and external clients. The problems we tackle in these projects are wide ranging and cut across various industries; the end goal is a production system that translates data into business impact. What is Enterprise Grade Machine Learning? Enterprise grade ML, a term mentioned in a paper put forth by Microsoft, refers to ML applications where there is a high level of scrutiny for data handling, model fairness, user privacy, and debuggability.
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Apr 1, 2020 · post

Bias in Knowledge Graphs - Part 1

by Keita · Introduction This is the first part of a series to review Bias in Knowledge Graphs (KG). We aim to describe methods of identifying bias, measuring its impact, and mitigating that impact. For this part, we’ll give a broad overview of this topic. image credit: Mediamodifier from Pixabay Motivation Knowledge graphs, graphs with built-in ontologies, create unique opportunities for data analytics, machine learning, and data mining. They do this by enhancing data with the power of connections and human knowledge.
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Reports

In-depth guides to specific machine learning capabilities

Prototypes

Machine learning prototypes and interactive notebooks
Library

NeuralQA

A usable library for question answering on large datasets.
https://neuralqa.fastforwardlabs.com
Notebook

Explain BERT for Question Answering Models

Tensorflow 2.0 notebook to explain and visualize a HuggingFace BERT for Question Answering model.
https://colab.research.google.com/drive/1tTiOgJ7xvy3sjfiFC9OozbjAX1ho8WN9?usp=sharing
Notebooks

NLP for Question Answering

Ongoing posts and code documenting the process of building a question answering model.
https://qa.fastforwardlabs.com
Notebook

Interpretability Revisited: SHAP and LIME

Explore how to use LIME and SHAP for interpretability.
https://colab.research.google.com/drive/1pjPzsw_uZew-Zcz646JTkRDhF2GkPk0N

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Cloudera Fast Forward is an applied machine learning reseach group.
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