Blog

Nov 30, 2017 · newsletter

The promise of Automated Machine Learning (AutoML)

Earlier this month The New York Times published an article on Building A.I. That Can Build A.I.. There is a lot of excitement about AutoML due to the scarcity of machine learning (ML) talent:

By some estimates, only 10,000 people worldwide have the education, experience and talent needed to build the complex and sometimes mysterious mathematical algorithms that will drive this new breed of artificial intelligence.

Furthermore, ML/AI experts are expensive: Tech Giants Are Paying Huge Salaries for Scarce A.I. Talent. AutoML promises more ML at lower cost; it is an enticing offering.

The multiple meanings of AutoML

That said, the realistic promise of new capabilities is hard to grasp. There are at least three different notions of AutoML:

  • Citizen Data Science / ML: AutoML will allow everyone to do data science and ML. It requires no special training or skills.
  • Efficient Data Science / ML: AutoML will supercharge your data scientists and ML engineers by making them more efficient.
  • Learning to Learn: AutoML will automate architecture and optimization algorithm design (much like neural networks automated feature engineering).

We could add a fourth:

  • Transfer Learning: AutoML will allow algorithms to learn new tasks faster by utilizing what they have learned from mastering other tasks in the past.

Google Brain’s AutoML project is about Learning to Learn:

Typically, our machine learning models are painstakingly designed by a team of engineers and scientists. This process of manually designing machine learning models is difficult because the search space of all possible models can be combinatorially large — a typical 10-layer network can have ~1010 candidate networks! For this reason, the process of designing networks often takes a significant amount of time and experimentation by those with significant machine learning expertise.

Learning to learn is very exciting! But it requires extensive computational resources for model training, the kind Google has access to, but not many others. By providing access to (cloud) compute power, of course, AutoML as Learning to Learn is an excellent strategy to monetize Google’s cloud compute offering; Google has an excellent business case for investing time and resources into the AutoML project (of course). But experts suugest it will take a while before the promise of AutoML as Learning to Learn will materialize:

Renato Negrinho, a researcher at Carnegie Mellon University who is exploring technology similar to AutoML, said this was not a reality today but should be in the years to come. “It is just a matter of when,” he said.

We agree. So how about the promise of the other notions of AutoML?

There are data science and ML platform vendors that promise to automate data science and ML to the extent that Citizen Data Science / ML may soon became real (e..g, DataRobot). Data science and ML practitioners, however, are skeptical about the promise of Citizen Data Science (and, frankly, worried about some of its outputs and consequences).

We believe AutoML as Efficient Data Science / ML shows real promise for the largest number of companies within the near to midterm future. There is ample opportunity to improve the data science and ML work flow, and to automate parts of it, to make your data professionals more effective.

The promise of AutoML as Efficient Data Science

The typical ML system can be broken down into a number of different components, or modules, each with a different aim and focus.

The different components of ML systems. Only a small fraction of real-world ML systems are composed of the ML code. To put ML to work requires complex surrounding infrastructure (taken from the paper Hidden Technical Debt in Machine Learning Systems).

ML code, while important, is only a small fraction of the code base authored by data teams (and their colleagues) to put algorithms to work. And, even the best, highest accuracy models are useful only in production. In production, they need monitoring, and (eventually) retraining. Building and maintaining these often fragile ML pipelines is expensive, both in time and effort. In the process, teams often build up significant technical debt.

Uber and Google recently published papers describing their ML platforms. Their platforms inform us about the challenges they faced putting ML to work.

Google’s platform is built with an emphasis on systems capable of detecting failure and bugs so they do not propagate into the production environment. Uber’s emphasis is on making good use for institutional knowledge. Uber’s platform features a feature store, where Uber’s data scientists store and share (engineered) features (and, presumably, trained models) alongside the appropriate meta-data to help with discoverability (preparing the ground for Transfer Learning). Both provide a framework for reliably producing and deploying machine learning models at scale and promise AutoML as Efficient Data Science / ML (and, eventually, Transfer Learning).

At Cloudera (please excuse the plug), the Cloudera Data Science Workbench provides a solution available to all, not just Google’s or Uber’s data scientists.

Read more

Newer
Dec 20, 2017 · post
Older
Nov 22, 2017 · post

Latest posts

Sep 22, 2021 · post

Automatic Summarization from TextRank to Transformers

by Melanie Beck · Automatic summarization is a task in which a machine distills a large amount of data into a subset (the summary) that retains the most relevant and important information from the whole. While traditionally applied to text, automatic summarization can include other formats such as images or audio. In this article we’ll cover the main approaches to automatic text summarization, talk about what makes for a good summary, and introduce Summarize. – a summarization prototype we built that showcases several automatic summarization techniques.
...read more
Sep 21, 2021 · post

Extractive Summarization with Sentence-BERT

by Victor Dibia · In extractive summarization, the task is to identify a subset of text (e.g., sentences) from a document that can then be assembled into a summary. Overall, we can treat extractive summarization as a recommendation problem. That is, given a query, recommend a set of sentences that are relevant. The query here is the document, relevance is a measure of whether a given sentence belongs in the document summary. How we go about obtaining this measure of relevance varies (a common dilemma for any recommendation system).
...read more
Sep 20, 2021 · post

How (and when) to enable early stopping for Gensim's Word2Vec

by Melanie Beck · The Gensim library is a staple of the NLP stack. While it primarily focuses on topic modeling and similarity for documents, it also supports several word embedding algorithms, including what is likely the best-known implementation of Word2Vec. Word embedding models like Word2Vec use unlabeled data to learn vector representations for each token in a corpus. These embeddings can then be used as features in myriad downstream tasks such as classification, clustering, or recommendation systems.
...read more
Jul 7, 2021 · post

Exploring Multi-Objective Hyperparameter Optimization

By Chris and Melanie. The machine learning life cycle is more than data + model = API. We know there is a wealth of subtlety and finesse involved in data cleaning and feature engineering. In the same vein, there is more to model-building than feeding data in and reading off a prediction. ML model building requires thoughtfulness both in terms of which metric to optimize for a given problem, and how best to optimize your model for that metric!
...read more
Jun 9, 2021 ·

Deep Metric Learning for Signature Verification

By Victor and Andrew. TLDR; This post provides an overview of metric learning loss functions (constrastive, triplet, quadruplet, and group loss), and results from applying contrastive and triplet loss to the task of signature verification. A complete list of the posts in this series is outlined below: Pretrained Models as Baselines for Signature Verification -- Part 1: Deep Learning for Automatic Offline Signature Verification: An Introduction Part 2: Pretrained Models as Baselines for Signature Verification Part 3: Deep Metric Learning for Signature Verification In our previous blog post, we discussed how pretrained models can serve as strong baselines for the task of signature verification.
...read more
May 27, 2021 · post

Pretrained Models as a Strong Baseline for Automatic Signature Verification

By Victor and Andrew. Figure 1. Baseline approach for automatic signature verification using pretrained models TLDR; This post describes how pretrained image classification models can be used as strong baselines for the task of signature verification. The full list of posts in the series is outlined below: Pretrained Models as Baselines for Signature Verification -- Part 1: Deep Learning for Automatic Offline Signature Verification: An Introduction Part 2: Pretrained Models as Baselines for Signature Verification Part 3: Deep Metric Learning for Signature Verification As discussed in our introductory blog post, offline signature verification is a biometric verification task that aims to discriminate between genuine and forged samples of handwritten signatures.
...read more

Popular posts

Oct 30, 2019 · newsletter
Exciting Applications of Graph Neural Networks
Nov 14, 2018 · post
Federated learning: distributed machine learning with data locality and privacy
Apr 10, 2018 · post
PyTorch for Recommenders 101
Oct 4, 2017 · post
First Look: Using Three.js for 2D Data Visualization
Aug 22, 2016 · whitepaper
Under the Hood of the Variational Autoencoder (in Prose and Code)
Feb 24, 2016 · post
"Hello world" in Keras (or, Scikit-learn versus Keras)

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

About

Cloudera Fast Forward is an applied machine learning reseach group.
Cloudera   Blog   Twitter