Notes about Cutting-Edge Technologies and Everything

Millions of Uber trips take place each day across nearly 80 countries, generating information on traffic, preferred routes, estimated times of arrival/delivery, drop-off locations, and more that enables us to facilitate better experiences for users. To make our data exploration and analysis more streamlined and efficient, we built Uber’s data science workbench (DSW), an all-in-one toolbox for interactive analytics and machine learning that leverages aggregate data. DSW centralizes everything a data scientist needs to perform data exploration, data preparation, ad-hoc analyses, model exploration, workflow scheduling, dashboarding, and collaboration in a single-pane, web-based graphical user interface (GUI). Leveraged by data science, engineering, and operations teams across the company, DSW has quickly scaled to become Uber’s go-to data analytics solution. Current DSW use cases include pricing, safety, fraud detection, and navigation, among other foundational elements of the trip experi...

In a recent survey—AI Adoption in the Enterprise, which drew more than 1,300 respondents—we found significant usage of several machine learning (ML) libraries and frameworks. About half indicated they used TensorFlow or scikit-learn, and a third reported they were using PyTorch or Keras. I recently attended an interesting RISELab presentation delivered by Caroline Lemieux describing recent work on AutoPandas and automation tools that rely on program synthesis. In the course of her presentation, Lemieux reviewed usage statistics they had gathered on different deep learning frameworks and data science libraries. She kindly shared some of that data with me, which I used to draw this chart: The numbers are based on simple full-text searches of papers posted on the popular e-print service arXiv.org. Specifically, they reflect the number of papers which mention (in a full-text search) each of the frameworks. Using this metric, the two most popular deep learning frameworks among resear...

Datanami  is pleased to announce the results of its fourth annual Readers’ and Editors’ Choice Awards, which recognizes the companies, products, and projects that have made a difference in the big data community this year. These awards, which are nominated and voted on by Datanami readers, give us insight into the state of the community. We’d like to thank our dedicated readers for weighing in on their top picks for the best in big data. It’s been a privilege for us to present these awards, and we extend our congratulations to this year’s winners. Best Big Data Product or Technology: Machine Learning Readers’ Choice: Elastic Editor’s Choice: SAS Visual Data Mining & Machine Learning Best Big Data Product or Technology: Internet of Things Readers’ Choice: SAS Analytics for IoT Editor’s Choice:  The Striim Platform Best Big Data Product or Technology: Big Data Security Readers’ Choice: Cloudera Enterprise Editor’s Choice: Elastic Stack Best Big ...

Lyft was a late entrant to the ride-sharing business model, at least compared to its competitor Uber, which pioneered the concept and remains the largest provider. That delay in starting out actually gave Lyft a bit of an advantage in terms of architecting its big data infrastructure in the cloud, as it was able to sidestep some of the challenges that Uber faced in building out its on-prem system. Lyft and Uber, like many of the young Silicon Valley companies shaking up established business models, aren’t shy about sharing information about their computer infrastructure. They both share an ethos of openness in regards to using and developing technology. That openness is also pervasive at Google, Facebook, Twitter, and other Valley outfits that created much of the big data ecosystem, most of which is, of course, open source. So when the folks at Lyft were blueprinting how to construct a system that could do all the things that a ride-sharing app has to do – tracking and connectin...

O’Reilly and TensorFlow are teaming up for the first-ever TensorFlow World, happening October 28–31 in Santa Clara. Where today's top minds bring machine learning to life From data centers to edge devices, and diagnosing diseases to environmental conservation, TensorFlow is powering the machine learning revolution. Growing from its origins at Google, TensorFlow is a fast-moving and expansive open source ecosystem, covering many platforms and programming languages in industry, education, and research. O'Reilly Media and TensorFlow are teaming up to present the first TensorFlow World, bringing together the entire community to explore the latest developments, from research to production, and application areas spanning healthcare, finance, robotics, IoT, and more. We'll hear how data scientists, engineers, developers, and product managers are leveraging TensorFlow to build products and services to help transform their company. Executives, CTOs, and innovators will share ...

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Google has released  TensorFlow Privacy , a free Python library that lets people train TensorFlow models compliant with more stringent user data privacy standards. It uses differential privacy, a technique for training machine learning systems that increases user privacy by letting developers set various trade-offs relating to the amount of noise applied to the user data being processed. This repository contains the source code for TensorFlow Privacy, a Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy. The library comes with tutorials and analysis tools for computing the privacy guarantees provided. The TensorFlow Privacy library is under continual development, always welcoming contributions. In particular, we always welcome help towards resolving the issues currently open.

Tools:  Python 3.6, PyAudio, TensorFlow, Deep Speech, Shell, Apache NiFi Why : Speech-to-Text Use Case:  Voice control and recognition. Series : Holiday Use Case: Turn on Holiday Lights and Music on command. Cool Factor:  Ever want to run a query on Live Ingested Voice Commands? Other Options: Voice Controlled with AIY Voice and NiFi We are using Python 3.6 to write some code around PyAudio, TensorFlow, and Deep Speech to capture audio, store it in a wave file, and then process it with Deep Speech to extract some text. This example is running in OSX without a GPU on Tensorflow v1.11. The Mozilla Github repo for their Deep Speech implementation has nice getting-started information that I used to integrate our flow with Apache NiFi. Apache NiFi Flow   Read full article >>>  

Comparing Top Deep Learning Frameworks: Deeplearning4j, PyTorch, TensorFlow, Caffe, Keras, MxNet, Gluon & CNTK Skymind bundles Deeplearning4j and Python deep learning libraries such as Tensorflow and Keras (using a managed Conda environment) in the Skymind Intelligence Layer (SKIL), which offers ETL, training and one-click deployment on a managed GPU cluster. The SKIL Community Edition is free and downloadable here . Eclipse Deeplearning4j is distinguished from other frameworks in its API languages, intent and integrations. DL4J is a JVM-based, industry-focused, commercially supported, distributed deep-learning framework that solves problems involving massive amounts of data in a reasonable amount of time. It integrates with Kafka, Hadoop and Spark using an arbitrary number of GPUs or CPUs , and it has a number you can call if anything breaks. DL4J is portable and platform neutral, rather than being optimized on a specific cloud service such as AWS, Azure or Goog...