JupyterHub on Google Cloud

JupyterHub + Kubernetes (source: O’Reilly)Meet the team behind JupyterHub at JupyterCon on August 22-25, 2017, in New York City. Registration is now open. JupyterHub, a “multi-user server for Jupyter Notebooks,” is an essential tool for teaching and training at scale with Jupyter. As described in The course of the future – and the technology behind it , JupyterHub is being used to power an introductory class in data science taken by hundreds of students at Berkeley every semester. JupyterLab is a complex piece of software, and setting up and operating it has been out of reach for many organizations, but recent work by members of the Jupyter team—especially @CarolWilling, @choldgraf, @Mbussonn, @minrk, and @yuvipanda—has put JupyterHub within reach of a host of organizations and individuals. Their new project, a Helm package for JupyterHub and an accompanying article called Zero to JupyterHub…
Original Post: JupyterHub on Google Cloud

Why AI and machine learning researchers are beginning to embrace PyTorch

Anatomy. (source: Pixabay)Learn how companies are using deep learning and AI by attending the Artificial Intelligence Conference in San Francisco, September 17-20, 2017. Save 20% with the code BIGDATA20. Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the Data Show, I spoke with Soumith Chintala, AI research engineer at Facebook. Among his many research projects, Chintala was part of the team behind DCGAN (Deep Convolutional Generative Adversarial Networks), a widely cited paper that introduced a set of neural network architectures for unsupervised learning. Our conversation centered around PyTorch, the successor to the popular Torch scientific computing framework. PyTorch is a relatively new deep learning framework that is fast becoming popular among researchers. Like Chainer, PyTorch supports…
Original Post: Why AI and machine learning researchers are beginning to embrace PyTorch

A DevOps approach to data management

Mosaic. (source: Pixabay)Download the free O’Reilly report “Defining Data-Driven Software Development,” by Eric Laquer.Deriving knowledge from data has become a key competency for many—if not most—businesses. With the right data, and the right tools to handle it, businesses can gain keen insights into a variety of metrics, including operations, customer activity, and employee productivity. In the free O’Reilly report, Defining Data-Driven Software Development, author Eric Laquer explores the advancements of DevOps and applies those lessons to managing data, addressing the challenges involved in handling business data and examining ways to fulfill the various needs of different stakeholders. Laquer also illustrates how using a multi-model approach allows for a variety of data types and schemas to operate side by side. Utilizing data in its natural form Multi-model technology accepts that our data comes in many types. It enables a variety of…
Original Post: A DevOps approach to data management

The wisdom hierarchy: From signals to artificial intelligence and beyond

Pyramid pattern. (source: Pixabay)In “Learning to Love Data Science,” Mike Barlow looks at how organizations are using data science to turn information into wisdom and business value. Download a collection of free chapters from “Learning to Love Data Science.” We are swimming in data. Or possibly drowning in it. Organizations and individuals are generating and storing data at a phenomenal and ever-increasing rate. The volume and speed of data collection has given rise to a host of new technologies and new roles focused on dealing with this data, managing it, organizing it, storing it. But we don’t want data. We want insight and value. We can think about these new technologies and roles, and the way they help us move from data to insight and value, through the lens of something called the wisdom hierarchy. The wisdom hierarchy is a…
Original Post: The wisdom hierarchy: From signals to artificial intelligence and beyond

How can I add simple, automated data visualizations and dashboards to Jupyter Notebooks

The IBM Watson Developer Advocacy team has developed an open source tool called PixieDust, which makes creating data visualizations and dashboards quick and easy in Jupyter Notebooks—something that previously required in-depth knowledge of libraries such as matplotlib or Seaborn. In this video, David Taieb gives you a high-level overview of PixieDust and explains how you can use it to increase your productivity with easy-to-create visualizations of your data right in your notebooks, allowing you to explore your data without needing a computer science degree to build data charts and graphs! Join David Taieb, Prithwish Chakraborty, and Faisal Farooq for a more comprehensive overview of PixieDust and InsightFactory at their JupyterCon session on August 24, 2017, in New York. Article image: Screen from “How can I add simple, automated data visualizations and dashboards to Jupyter Notebooks?” (source: O’Reilly).
Original Post: How can I add simple, automated data visualizations and dashboards to Jupyter Notebooks

R’s tidytext turns messy text into valuable insight

Woodtype (source: Pixabay)Check out “Text Mining with R: A tidy approach” to learn about how tidy data principles and the tidytext package can help you perform text mining in R. “Many of us who work in analytical fields are not trained in even simple interpretation of natural language,” write Julia Silge, Ph.D., and David Robinson, Ph.D., in their newly released book Text Mining with R: A tidy approach. The applications of text mining are numerous and varied, though; sentiment analysis can assess the emotional content of text, frequency measurements can identify a document’s most important terms, analysis can explore relationships and connections between words, and topic modeling can classify and cluster similar documents. I recently caught up with Silge and Robinson to discuss how they’re using text mining on job postings at Stack Overflow, some of the challenges and best…
Original Post: R’s tidytext turns messy text into valuable insight

A lesson in prescriptive modeling

This is a video excerpt from “Hands-On Techniques for Business Model Simulation,” by Jerry Overton, which is part of the Creating Simulations to Discover New Business Models Learning Path. Access the full video and Learning Path on Safari. For the data professional, the first step to mastering prescriptive modeling is to understand simulation. In this excerpt from the O’Reilly video Hands-On Techniques for Business Model Simulation, I’ll walk you through a practical case study—simulating the cross-breeding of a new species of iris, and new business models for the resulting flowers. Using published open source code, viewers learn to generate a new species of iris, find interesting new characteristics, and search through business model simulations for profitable ways of bringing the new flowers to the market. It takes a lot of knowledge and skill to create useful simulations of the real…
Original Post: A lesson in prescriptive modeling

Data science startups focus on AI-enabled efficiency

Winners. (source: Pixabay)Check out the AI-related sessions coming up at the Strata Data Conference in New York, September 25-28, 2017. Every five years, we invent a new technology that, when sprinkled atop existing business problems, acts as a panacea for managers. In the ‘90s it was the Web, followed quickly by SaaS, mobility, clouds, data, and now AI. But there’s a bigger underlying pattern. Web and SaaS gave us interfaces anyone could use. Mobility made them ubiquitous, taking us away from the workday—we check our phones dozens of times a day, and, often, they’re the last thing we look at before sleep and the first thing we grab upon waking. Clouds gave us elastic, on-demand computing. Big data gave clouds something to do. And AI is a set of algorithms that make sense of that big data, teasing threads of…
Original Post: Data science startups focus on AI-enabled efficiency

How big data and AI will reshape the automotive industry

Evolution of the bicycle (source: Al2 on Wikimedia Commons)For more on the role of big data and analytics in transportation, check out the transportation and autonomous vehicles sessions at the Artificial Intelligence Conference in San Francisco, September 17-20, 2017. Best price ends August 4. Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the Data Show, I spoke with Evangelos Simoudis, co-founder of Synapse Partners and a frequent contributor to O’Reilly. He recently published a book entitled The Big Data Opportunity in Our Driverless Future, and I wanted get his thoughts on the transportation industry and the role of big data and analytics in its future. Simoudis is an entrepreneur, and he also advises and invests in…
Original Post: How big data and AI will reshape the automotive industry

Adopting AI in the Enterprise: Ford Motor Company

Fractal art (source: Pixabay)Check out the Strata Business Summit at the Strata Data Conference in New York City, Sept. 25-28, 2017, to learn more from data-driven businesses—including American Express, BBC Worldwide, and LinkedIn. Early price ends August 11. Driverless cars aren’t the only application for deep learning on the road: neural networks have begun to make their way into every corner of the automotive industry, from supply-chain management to engine controllers. In this installment of our ongoing series on artificial intelligence (AI) and machine learning (ML) in the enterprise, we speak with Dimitar Filev, executive technical leader at Ford Research & Advanced Engineering, who leads the team focused on control methods and computational intelligence. What was the first application of AI and ML at Ford? Ford research lab has been conducting systematic research on computational intelligence—one of the branches of…
Original Post: Adopting AI in the Enterprise: Ford Motor Company