Aug 29, 2018 · newsletter

Automated Machine Learning: Hype now, reality later?

Previously in our newsletter, we had framed automated machine learning around four notions:

  • Citizen Data Science / ML: Automated machine learning will allow everyone to do data science and ML. It requires no special training or skills.
  • Efficient Data Science / ML: Automated machine learning will supercharge your data scientists and ML engineers by making them more efficient.
  • Learning to Learn: Automated machine learning will automate architecture and optimization algorithm design(architecture search).
  • Transfer Learning: Automated machine learning will allow algorithms to learn new tasks faster by utilizing what they learned from mastering other tasks in the past.

Since then, the term automated machine learning has been strongly linked to Google’s definition of AutoML as a way for neural nets to design neural nets, or - expressed technically - as a way to perform neural architecture search. Google’s messaging asserts that AutoML will make AI work for everyone. Google Cloud’s AutoML beta products now allow one to custom vision, language and translation models with minimum machine learning skills. The product page states that under the hood, this capability is powered by Google’s AutoML and transfer learning. But, as pointed out by, transfer learning and neural architecture search are two opposite approaches. Transfer learning assumes that neural net architectures generalize to similar problems (for example, features like corners and lines show up in many different images); neural architecture search assumes that each dataset needs a unique and specialized architecture. In transfer learning, you start with a trained model with an existing architecture and further tune the weights with your data; neural architecture search requires training multiple new architectures along with new weights. In practice, one does not need to use both techniques (yet?)! Transfer learning is currently the predominant approach since neural architecture search is currently computationally expensive. We very much agree with’s assessment that not everyone needs to perform neural architecture search, and the ability to perform such a search does not replace machine learning expertise. In fact, blindly using computation power to search for the best architecture seems to lead us further into the abyss of un-interpretable models.

On the flip side, if we go back in time to the pre-GPU era, one could argue that we are at the same place with neural architecture search as we were back then with deep learning. Sprinkle in the notion of Software 2.0, and suddenly the idea of everyone designing neural nets for their particular needs looks like a reasonable trajectory!

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Aug 29, 2018 · newsletter
Aug 29, 2018 · newsletter

Latest posts

Jun 22, 2020 · post

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

by Victor · 199 // ... code -- 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. Recently, our team at Fast Forward Labs have been exploring state of the art models for Question Answering and have used the rather excellent HuggingFace transformers library. more
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.
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.
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:
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. more
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. more

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In-depth guides to specific machine learning capabilities


Machine learning prototypes and interactive notebooks


A usable library for question answering on large datasets.

Explain BERT for Question Answering Models

Tensorflow 2.0 notebook to explain and visualize a HuggingFace BERT for Question Answering model.

NLP for Question Answering

Ongoing posts and code documenting the process of building a question answering model.

Interpretability Revisited: SHAP and LIME

Explore how to use LIME and SHAP for interpretability.


Cloudera Fast Forward is an applied machine learning reseach group.
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