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AI and Data Science Presentations to Look Forward to at DataWorks Summit

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AI and Data Science Presentations to Look Forward to at DataWorks Summit

The upcoming DataWorks Summit has plenty for anyone interested in AI or data science. Jorge rounds up some of the most exciting talks so you can plan your trip around them!

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This year, I'm honored to be the chair of the artificial intelligence and data science track at the

Not surprisingly, the topics and tools around deep learning (DL) still top the list of big trends, and top-notch research in math and computation are driving progress across vision, speech, and text. Many in the DataWorks audience are already developing cutting-edge deep learning systems, while others are just beginning to start playing with DL. Either way, I suggest attending Magnus Hyttsten's talk on getting started with TensorFlow.

As you read this article, a new DL framework might already be baking and being open-sourced. It's getting harder and harder to keep track of all the new DL frameworks and their capabilities. The complexity can be daunting, especially if you just want to know which DL framework to use for a shiny new project at your company. If that sounds familiar, plan to attend Jeremy Nixon's talk for some insights about which DL framework to use and why.

Let's assume you've chosen a DL framework and your team of data scientists has created a quick and dirty prototype model on a sample of the data. Now, you're ready to train the model with a much larger dataset — but the system dies. If that sounds familiar, check out Wangda Tan's talk about running distributed TensorFlow.

Let's assume you've trained your model at scale. Now what? It's vital to actually deploy models in production systems — but not easy in practice. To learn more details about the open-source tools that companies are using to deploy models, check out the talks by Sriram Srinivasan and Sven Hafeneger.

I'm a big fan of SVD. In fact, a linear combination of SVD, coffee, and a ton of hours programming new ideas added up to my Ph.D. I have a particular fondness for SVD because it almost always helps me understand how hard a problem is, how easy it is to break the problem into a smaller one, or whether I need to check whether I need more or fewer degrees of freedom in a trained embedding model. I'm always looking for ways to use SVD for cool AI applications — which is a perfect reason to attend Trevor Grant's talk on real-time facial recognition using a distributed implementation of SVD.

Another topic that's catching my eye is graph-based machine learning methods, which are featured in two separate but related talks. First is Venkatesh Ramanathan's talk about how to automatically learn features using a network structure and use it for fraud prevention. Second is Namrata Ghadi's and Adam Baker's talk on how to use word embeddings and NLP techniques for job skill normalization. Maybe I'll get to use this technique to filter and organize resumes for my team's next batch of hiring. Most times, exploiting the structure of a problem, like graph-based methods do, helps improve the quality, the performance, or both.

I couldn't be more excited about the summit. Looking forward to seeing you there in just a few weeks.

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Topics:
machine learning ,deep learning ,conference ,data science ,ibm ,tensorflow ,ai

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