I started Fast Forward Labs more than three years ago with the vision of creating a new mechanism for applied research, helping businesses large and small understand how recently possible machine learning and applied artificial intelligence technologies could be useful for solving real business problems.
Since then, we’ve published reports, built prototypes, and advised organizations on how to think about their machine learning opportunities, strategically and technically. On the way, we built a profitable company with real impact on our clients’ products and businesses. I’m proud of what we’ve accomplished.
However, we’re just getting started. The enterprise is more excited about machine learning and applied artificial intelligence than ever, and in order to meet this growing opportunity, we are heading in a new direction.
Today, I’m pleased to share that Fast Forward Labs is merging with Cloudera.
A machine learning product requires data, algorithms (our specialty!), and infrastructure to all work harmoniously. We’re delighted to join forces with a company that drives progress in the foundational technologies our work relies on. By joining Cloudera, we will be able to bring the opportunities discovered in our research to life in new ways, at the scale of the Cloudera platform.
We will continue to offer our research and advisory services, and our existing clients will see no significant changes.
Finally, I’d like to take a moment to offer my thanks to everyone who has supported us through these last few years, including our brilliant Fast Forward Labs team, our wonderful clients, and our community.
To the future,
More from the Blog
Sep 1 2017
by — Henry VIII of England had many relationships. We build a classifier to predict whether relationships are going to last, or not, and used Local Interpretable Model-Agnostic Explanations (LIME) to understand the predicted success or failure of given relationships. Last month we launched the latest report and prototype from our machine intelligence R&D team, Interpretability, and we shared ...
Sep 11 2017
by — We’re pleased to share the recording of our recent webinar on machine learning interpretability and accompanying resources. We were joined by guests Patrick Hall (Senior Director for Data Science Products at H2o.ai, co-author of Ideas on Interpreting Machine Learning) and Sameer Singh (Assistant Professor of Computer Science at UC Irvine, co-creator of LIME). We spoke for an hour and got ...
Jul 22 2019
by — We discussed this research as part of our virtual event on Wednesday, July 24th; you can watch the replay here! Convolutional Neural Networks (CNNs or ConvNets) excel at learning meaningful representations of features and concepts within images. These capabilities make CNNs extremely valuable for solving problems in the image analysis domain. We can automatically identify defects in manufactur...