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Why you should care about debugging machine learning models

O'Reilly on Data

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

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Deep learning for improved breast cancer monitoring using a portable ultrasound scanner

Insight

The model was a modified U-Net and trained on GPU hosted by Amazon Web Services (AWS) EC2 instances. The loss function used is illustrated in the figure below, with “A” representing the ground truth (manually labeled mask) and “B” representing the model generated mask. Here, we built a model to mimic this process.

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Artificial intelligence and machine learning adoption in European enterprise

O'Reilly on Data

In this post, I’ll describe some of the key areas of interest and concern highlighted by respondents from Europe, while describing how some of these topics will be covered at the upcoming Strata Data conference in London (April 29 - May 2, 2019). Machine Learning model lifecycle management. Deep Learning.

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What a quarter century of digital transformation at PayPal looks like

CIO Business Intelligence

These applications live on innumerable servers, yet some technology is hosted in the public cloud. We’ve been working on this for over a decade, including transformer-based deep learning,” says Shivananda. PayPal’s deep learning models can be trained and put into production in two weeks, and even quicker for simpler algorithms.

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Microsoft’s latest OpenAI investment opens way to new enterprise services

CIO Business Intelligence

As OpenAI’s exclusive cloud provider it will see additional revenue for its Azure services, as one of OpenAI’s biggest costs is providing the computing capacity to train and run its AI models. The deal, announced by OpenAI and Microsoft on Jan. Additionally, it may not always be able to understand or respond to certain inputs correctly.”

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BRIDGEi2i Organizes AWS User Group Meetup on Computer Vision

bridgei2i

BANGALORE, May 14, 2019. BRIDGEi2i is pleased to host Alex Smola – VP & Distinguished Scientist at AWS for an informative and hands-on learning session on Computer Vision GluconCV & D2L.ai on 18th May 2019 at the BRIDGEi2i auditorium. For more details on the meetup, please click here. About Alex Smola.

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What you need to know about product management for AI

O'Reilly on Data

But there’s a host of new challenges when it comes to managing AI projects: more unknowns, non-deterministic outcomes, new infrastructures, new processes and new tools. A lot to learn, but worthwhile to access the unique and special value AI can create in the product space. Models also become stale and outdated over time.