Blog

Mar 31, 2017 · announcement

Free eBook: Development Workflows for Data Scientists

Cover image from Development Workflows for Data Scientists eBook

Working with over 30 enterprise clients, in industries like financial services, insurance, publishing, and retail, the Fast Forward Labs team has had ample opportunity to observe the challenges of doing data science in practice. By now, most organizations have moved beyond traditional waterfall software development process to adopt more risk-tolerant and agile methodologies. But directly applying agile to data science can create friction, as data products require more leeway for experimentation and exploration, as well as open communication between business, science, and engineering teams.

With so many data teams looking for guidance, we’re excited to see O’Reilly’s new (free!) eBook, Development Workflows for Data Scientists (PDF), which features insights from our Friederike Schuur. The book includes guidance on structuring teams, designing workflows, optimizing processes to learn from previous work, documenting outcomes, and communicating results to non-technical colleagues. Friederike, for example, contrasts the value documentation in standard software development versus experimental data product development:

In data science and machine learning you’re doing so many things before you know what actually works. You can’t just document the working solution. It’s equally valuable to know the dead ends. Otherwise, someone else will take the same approach.

Check out the book, and feel free to contact us at cffl@cloudera.com with questions about your own data science processes!

Read more

Newer
Apr 14, 2017 · demo
Older
Mar 25, 2017 · talk slides

Latest posts

Sep 8, 2022 · post

Thought experiment: Human-centric machine learning for comic book creation

by Michael Gallaspy · This post has a companion piece: Ethics Sheet for AI-assisted Comic Book Art Generation I want to make a comic book. Actually, I want to make tools for making comic books. See, the problem is, I can’t draw too good. I mean, I’m working on it. Check out these self portraits drawn 6 months apart: Left: “Sad Face”. February 2022. Right: “Eyyyy”. August 2022. But I have a long way to go until my illustrations would be considered professional quality, notwithstanding the time it would take me to develop the many other skills needed for making comic books.
...read more
Jul 29, 2022 · post

Ethical Considerations When Designing an NLG System

by Andrew Reed · Blog Series This post serves as Part 4 of a four part blog series on the NLP task of Text Style Transfer. In this post, we expand our modeling efforts to a more challenging dataset and propose a set of custom evaluation metrics specific to our task. Part 1: An Introduction to Text Style Transfer Part 2: Neutralizing Subjectivity Bias with HuggingFace Transformers Part 3: Automated Metrics for Evaluating Text Style Transfer Part 4: Ethical Considerations When Designing an NLG System At last, we’ve made it to the final chapter of this blog series.
...read more
Jul 11, 2022 · post

Automated Metrics for Evaluating Text Style Transfer

by Andrew & Melanie · By Andrew Reed and Melanie Beck Blog Series This post serves as Part 3 of a four part blog series on the NLP task of Text Style Transfer. In this post, we expand our modeling efforts to a more challenging dataset and propose a set of custom evaluation metrics specific to our task. Part 1: An Introduction to Text Style Transfer Part 2: Neutralizing Subjectivity Bias with HuggingFace Transformers Part 3: Automated Metrics for Evaluating Text Style Transfer Part 4: Ethical Considerations When Designing an NLG System In our previous blog post, we took an in-depth look at how to neutralize subjectivity bias in text using HuggingFace transformers.
...read more
May 5, 2022 · post

Neutralizing Subjectivity Bias with HuggingFace Transformers

by Andrew Reed · Blog Series This post serves as Part 2 of a four part blog series on the NLP task of Text Style Transfer. In this post, we expand our modeling efforts to a more challenging dataset and propose a set of custom evaluation metrics specific to our task. Part 1: An Introduction to Text Style Transfer Part 2: Neutralizing Subjectivity Bias with HuggingFace Transformers Part 3: Automated Metrics for Evaluating Text Style Transfer Part 4: Ethical Considerations When Designing an NLG System Subjective language is all around us – product advertisements, social marketing campaigns, personal opinion blogs, political propaganda, and news media, just to name a few examples.
...read more
Mar 22, 2022 · post

An Introduction to Text Style Transfer

by Andrew Reed · Blog Series This post serves as Part 1 of a four part blog series on the NLP task of Text Style Transfer. In this post, we expand our modeling efforts to a more challenging dataset and propose a set of custom evaluation metrics specific to our task. Part 1: An Introduction to Text Style Transfer Part 2: Neutralizing Subjectivity Bias with HuggingFace Transformers Part 3: Automated Metrics for Evaluating Text Style Transfer Part 4: Ethical Considerations When Designing an NLG System Today’s world of natural language processing (NLP) is driven by powerful transformer-based models that can automatically caption images, answer open-ended questions, engage in free dialog, and summarize long-form bodies of text – of course, with varying degrees of success.
...read more
Jan 31, 2022 · post

Why and How Convolutions Work for Video Classification

by Daniel Valdez-Balderas · Video classification is perhaps the simplest and most fundamental of the tasks in the field of video understanding. In this blog post, we’ll take a deep dive into why and how convolutions work for video classification. Our goal is to help the reader develop an intuition about the relationship between space (the image part of video) and time (the sequence part of video), and pave the way to a deep understanding of video classification algorithms.
...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

Cloudera Fast Forward Labs

Making the recently possible useful.

Cloudera Fast Forward Labs is an applied machine learning research group. Our mission is to empower enterprise data science practitioners to apply emergent academic research to production machine learning use cases in practical and socially responsible ways, while also driving innovation through the Cloudera ecosystem. Our team brings thoughtful, creative, and diverse perspectives to deeply researched work. In this way, we strive to help organizations make the most of their ML investment as well as educate and inspire the broader machine learning and data science community.

Cloudera   Blog   Twitter

©2022 Cloudera, Inc. All rights reserved.