In this episode of the Data Show, I spoke with Jason Dai, CTO of big data technologies at Intel, and co-chair of Strata + Hadoop World Beijing. Dai and his team are prolific and longstanding contributors to the Apache Spark project. Their early contributions to Spark tended to be on the systems side and included Netty-based shuffle, a fair-scheduler, and the “yarn-client” mode. Recently, they have been contributing tools for advanced analytics. In partnership with major cloud providers in China, they’ve written implementations of algorithmic building blocks and machine learning models that let Apache Spark users scale to extremely high-dimensional models and large data sets. They achieve scalability by taking advantage of things like data sparsity and Intel’s MKL software. Along the way, they’ve gained valuable experience and insight into how companies deploy machine learning models in real-world applications.
When I predicted that 2017 would be the year when the big data and data science communities start exploring techniques like deep learning in earnest, I was relying on conversations with many members of those communities. I also knew that Dai and his team were at work on a distributed deep learning library for Apache Spark. This evolution from basic infrastructure, to machine learning applications, and now applications backed by deep learning models is to be expected.
Once you have a platform and a team that can deploy machine learning models, it’s natural to begin exploring deep learning. As I’ve highlighted in recent episodes of this podcast (here and here), companies are beginning to apply deep learning to time-series data, event data, text, and images. Many of these same companies have already invested in big data technologies (many of which are open source) and employ data scientists and data engineers who are comfortable with these tools. Continue reading →
As data scientists add deep learning to their arsenals, they need tools that integrate with existing platforms and frameworks. This is particularly important for those who work in large enterprises. In this episode of the Data Show, I spoke with Adam Gibson, co-founder and CTO of Skymind, and co-creator of Deeplearning4J (DL4J). Gibson has spent the last few years developing the DL4J library and community, while simultaneously building deep learning solutions and products for large enterprises.
Putting deep learning into practice with new tools, frameworks, and future developments.
Deep learning has made tremendous advances in the past year. Though managers are aware of what’s been happening in the research world, we’re still in the early days of putting that research into practice. While the resurgence in interest stems from applications in computer vision and speech, more companies can actually use deep learning on data they already have—including structured data, text, and times-series data.
All of this interest in deep learning has led to more tools and frameworks, including some that target non-experts already using other forms of machine learning (ML). Many devices will benefit from these technologies, so expect streaming applications to be infused with intelligence. Finally, there are many interesting research initiatives that point to future neural networks, with different characteristics and enhanced model-building capabilities.
Back to machine learning
If you think of deep learning as yet another machine learning method, then the essential ingredients should be familiar. Software infrastructure to deploy and maintain models remains paramount. A widely cited paper from Google uses the concept of technical debt to posit that “only a small fraction of real-world ML systems is composed of ML code.” This means that while underlying algorithms are important, they tend to be a small component within a complex production system. As the authors point out, machine learning systems also need to address ML-specific entanglement and dependency issues involving data, features, hyperparameters, models, and model settings (they refer to this as the CACE principle: Changing Anything Changes Everything). Continue reading →
Specialists describe deep learning as akin to a rocketship that needs a really big engine (a model) and a lot of fuel (the data) in order to go anywhere interesting. To get a better understanding of the issues involved in building compute systems for deep learning, I spoke with one of the foremost experts on this subject: Greg Diamos, senior researcher at Baidu. Diamos has long worked to combine advances in software and hardware to make computers run faster. In recent years, he has focused on scaling deep learning to help advance the state-of-the-art in areas like speech recognition.
A big model, combined with big data, necessitates big compute—and at least at the bleeding edge of AI, researchers have gravitated toward high-performance computing (HPC) or supercomputer-like systems. Most practitioners use systems with multiple GPUs (ASICs or FPGAs) and software libraries that make it easy to run fast deep learning models on top of them.
In keeping with the convenience versus performance tradeoff discussions that play out in many enterprises, there are other efforts that fall more in the big data, rather than HPC, camp. In upcoming posts, I’ll highlight groups of engineers and data scientists who are starting to use these techniques and are creating software to run them on existing software and hardware infrastructure common in the big data community.
In this episode of the O’Reilly Data Show, I spoke with Christopher Nguyen, CEO and co-founder of Arimo. Nguyen and Arimo were among the first adopters and proponents of Apache Spark, Alluxio, and other open source technologies. Most recently, Arimo’s suite of analytic products has relied on deep learning to address a range of business problems.
While I was in Beijing for Strata + Hadoop World, several people reminded me of the chatbot Xiaoice—one of the most popular accounts on the Chinese social media site Weibo. Developed by Microsoft researchers, Xiaoice comes with a personality and is able to engage users in extended conversations on Weibo. These types of capabilities highlight that in an attention economy, systems that are able to forge an emotional connection will garner more loyalty and engagement from users.
In this episode of the O’Reilly Data Show, I sat down with Rana el Kaliouby, co-founder and CEO of Affectiva, one of the leading experts in emotion sensing systems. We talked about the impact of deep learning and computer vision, Affectiva’s large facial expression database, and privacy and ethics in an era of multimodal systems.