Blog

Dec 15, 2015 · post

NextView Ventures Podcast

Boston-based NextView Ventures runs a podcast series called Traction that features interviews with exciting new startups. Their latest podcast features an interview with our own Hilary Mason. Some highlights:

In its essence, data science is the practice of learning insights from a data set and building a product based upon these learnings.

The Fast Forward Labs team starts every data advisory engagement, be this with small startup or large enterprise clients, with a data census comprising three questions:

What data do we have? This is often harder to answer than it seems, especially in large enterprises with silo’d departments. We have our clients look at their products to write down what data they collect and look at their servers to inventory what kinds of data they store.

What data should we have? Here, we think about how the business runs and what types of questions are important for growth and success. We then ask clients to think about where they would store new data they may collect, be that in a Hadoop cluster, in Amazon’s S3, or on internal servers.

What assumptions have we made that we can now validate with data? Many companies have intuitions about market penetration and product opportunities. Analysis can verify or challenge these assumptions, opening new avenues for growth.

Fast Forward Labs was founded to provide a new vehicle for applied research, bridging the startup community with large enterprise to test and amplify machine learning technologies that will be impactful in the upcoming years.

The most important lesson Hilary has learned of late is how important it is to understand the history and evolution of different technologies. Contemporary programming environments are abstractions upon abstractions that can generate bizarre behavior when they hit edge cases. It’s important to grasp some of the historical quirks to resolve coding challenges.

Listen to the entire podcast [here](href="https://soundcloud.com/nextview/15-skype-side-chat-on-data-science-inventing-the-future-hilary-mason-fast-forward-labs).

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Nov 15, 2022 · newsletter

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Oct 20, 2022 · newsletter

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Sep 21, 2022 · newsletter

CFFL September Newsletter

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Sep 8, 2022 · post

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Reports

In-depth guides to specific machine learning capabilities

Prototypes

Machine learning prototypes and interactive notebooks
Notebook

ASR with Whisper

Explore the capabilities of OpenAI's Whisper for automatic speech recognition by creating your own voice recordings!
https://colab.research.google.com/github/fastforwardlabs/whisper-openai/blob/master/WhisperDemo.ipynb
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

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.

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