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Sep 28, 2018 · newsletter

The True Competitive Advantage in ML

Data science, machine learning (ML), and AI are no longer just cute buzzwords. Nearly all organizations, companies and governments now recognize the immense potential of AI-enabled products and services, and many of them have already made the very real investment of hiring employees with skills in these emerging fields.

However, as is true with most things in data science, one size does not fit all! Simply hiring a self-proclaimed data scientist or machine learning expert with advanced degrees isn’t likely to fit the bill. A successful machine learning application often requires looking at a business problem critically, crafting a creative technical solution to that problem, designing and executing (many) experiments to see if that solution is possible, and then (if so) developing a scalable way to integrate that solution into an application framework. Expecting a single person to have all of these skills (and also to want to apply them all at the same time) is a bit much.

Of course, the answer is to create a team full of highly competent people with creative, technical and product skills, who are all passionate about AI/ML, right? Easier said than done!

photo by rawpixel on Unsplash

It’s becoming more and more clear that the true competitive advantage in AI/ML isn’t some killer algorithm, or even a cutting-edge technology platform (complete with robust experimentation capabilities and slick visualizations); it’s the ability to build, engage, and retain an awesome team.

Much has been written on the best ways to structure data science organizations (DJ Patil even wrote a book on the subject!) But what are the roles that make up these prolific groups, and more importantly, how should leaders best apply the rare skills that each of these roles bring to the table?

In an article on the subject earlier this summer, Forbes describes three critical roles: data scientists, data engineers, and machine learning engineers. These roles and team structure are great for smaller but agile organizations seeking to uncover AI/ML use cases and then rapidly address them with scrappy solutions.

Hackernoon takes a more detailed approach to describing AI/ML roles in an article describing the Top 10 Roles in AI and data science. Staffing out a team or team(s) with these skills and responsibilities might be more appealing (and realistic) for a more mature department, especially when enterprise software engineering, compliance and/or regulatory requirements, are a major consideration.

Ultimately, there are no cookie-cutter AI/ML team structures, and thus no perfect roles. But as the community of practice grows and enjoys more and more success, best practices have certainly emerged. For example, here at Cloudera Fast Forward Labs, we often work with our customers to help them discover the right AI/ML vision, team structure, core skills, and enabling technology necessary to reach their respective AI/ML goals–and beyond!

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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.
...read 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.
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.
qa.fastforwardlabs.com
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.
...read 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.
...read more

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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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