How artificial intelligence is changing advertising in China: A conversation with Bessie Lee and Ching Law
February 3, 2020
Advertising in China is on the frontline of AI adoption and innovation. Join Bessie Lee and Ching Law for a conversation on how AI is changing advertising. You'll hear how China's white-hot AI advertising applications can serve as roadmaps and spark ideas in other industries and how companies like Tencent are improving performance by leveraging AI technology.
Executive Briefing: Why machine-learned models crash and burn in production and what to do about it
January 23, 2020
Machine learning and data science systems often fail in production in unexpected ways. David Talby shares real-world case studies showing why this happens and explains what you can do about it, covering best practices and lessons learned from a decade of experience building and operating such systems at Fortune 500 companies across several industries.
Leveraging Spark and deep learning frameworks to understand data at scale
January 21, 2020
Vartika Singh, Alan Silva, Alex Bleakley, Steven Totman, Mirko Kmpf, and Syed Nasar outline approaches for preprocessing, training, inference, and deployment across datasets (time series, audio, video, text, etc.) that leverage Spark, its extended ecosystem of libraries, and deep learning frameworks.
TuneIn: How to get your jobs tuned while you are sleeping
January 17, 2020
Have you ever tuned a Spark or MR job? If the answer is yes, you already know how difficult it is to tune more than hundred parameters to optimize the resources used. Manoj Kumar, Pralabh Kumar, and Arpan Agrawal offer an overview of TuneIn, an auto-tuning tool developed to minimize the resource usage of jobs. Experiments have shown up to a 50% reduction in resource usage.
Using Microservices Architecture and Patterns to Address Applications Requirements on MEC
January 16, 2020
Edge Computing Infrastructure needs to be closer to end-user yet provide ability to offload compute from End user devices for apps such that it can manage both real-time and lossless applications. MEC …
Reproducible quantum chemistry in Jupyter
January 6, 2020
In silico prediction of chemical properties has seen vast improvements in both veracity and volume of data but is currently hamstrung by a lack of transparent, reproducible workflows coupled with environments for visualization and analysis. Chris Harris offers an overview of a platform that uses Jupyter notebooks to enable an end-to-end workflow from simulation setup to visualizing the results.
Visualizing machine learning models in the Jupyter Notebook (sponsored by Bloomberg LP)
January 4, 2020
Chakri Cherukuri offers an overview of the interactive widget ecosystem available in the Jupyter notebook and illustrates how Jupyter widgets can be used to build rich visualizations of machine learning models. Along the way, Chakri walks you through algorithms like regression, clustering, and optimization and shares a wizard for building and training deep learning models with diagnostic plots.
Blockchains are the link between horseless buggies and driverless cars
January 2, 2020
Personal transportation is on the cusp of the first major revolution in 100 years. Valentin Bercovici discusses the unexpected role blockchains will play in giving us all mobility choices we soon won't be able to live without.
Distributed TensorFlow on Hops
December 30, 2019
Fabio Buso offers demonstrations of frameworks for building distributed TensorFlow applications on the Hops platform and walks you through the whole model lifecycle, from debugging and visualizing models on TensorBoard to parallel experimentation and distributed training (with the help of Spark) to model deployment and inferencing using TensorFlow Serving and Kubernetes.
Debugging frontend performance
December 16, 2019
Join Tim Kadlec, Gareth Hughes, and Michael Gooding to learn how to load the progressive web faster and get hands-on experience with the newest performance techniques. You'll cover the foundational browser concepts on the first day, particularly relating to performance and optimization; then, on the second day, you'll learn how to implement and optimize a progressive web app (PWA).
It's spelled "accessibility," not "disability"
December 15, 2019
What if you could increase your website's SEO, improve your mobile web design, and get a head start on the coming conversational UI revolution through a renewed focus on accessibility? And what if you increased your user base by making it more accessible to disabled users? Scott Davis explains why accessibility should be just as important to you as a mobile design strategy was 10 years ago.
To push, or not to push? The future of HTTP/2 server push
December 13, 2019
HTTP/2 server push gives us the ability to proactively send assets to a browser without waiting for them to be requested. Sounds great, but is this new mechanism really a silver bullet? Using new research and real-world examples, Patrick Hamann leads a deep dive into server push and attempts to answer the question we're all asking: Is it ready for production?
Practical advice for driving down the cost of cloud big data platforms
December 6, 2019
Big data and cloud deployments return huge benefits in flexibility and economics but can also result in runaway costs and failed projects. Drawing on his production experience, Christopher Royles shares tips and best practices for determining initial sizing, strategic planning, and longer-term operation, helping you deliver an efficient platform, reduce costs, and implement a successful project.
Scaling the AI hierarchy of needs with TensorFlow, Spark, and Hops
December 5, 2019
Distributed deep learning can increase the productivity of AI practitioners and reduce time to market for training models. Hadoop can fulfill a crucial role as a unified feature store and resource management platform for distributed deep learning. Jim Dowling offers an introduction to writing distributed DL applications, covering TensorFlow and Apache Spark frameworks that make distribution easy.
Using Alluxio as a fault-tolerant pluggable optimization component of JD.com's compute frameworks
December 4, 2019
Mao Baolong, Yiran Wu, and Yupeng Fu explain how JD.com uses Alluxio to provide support for ad hoc and real-time stream computing, using Alluxio-compatible HDFS URLs and Alluxio as a pluggable optimization component. To give just one example, one framework, JDPresto, has seen a 10x performance improvement on average.