"data science" entries
To understand deep learning, let’s start simple.
Use code DATA50 to get 50% off of the new early release of “Fundamentals of Deep Learning: Designing Next-Generation Artificial Intelligence Algorithms.” Editor’s note: This is an excerpt of “Fundamentals of Deep Learning,” by Nikhil Buduma.
The brain is the most incredible organ in the human body. It dictates the way we perceive every sight, sound, smell, taste, and touch. It enables us to store memories, experience emotions, and even dream. Without it, we would be primitive organisms, incapable of anything other than the simplest of reflexes. The brain is, inherently, what makes us intelligent.
The infant brain only weighs a single pound, but somehow, it solves problems that even our biggest, most powerful supercomputers find impossible. Within a matter of days after birth, infants can recognize the faces of their parents, discern discrete objects from their backgrounds, and even tell apart voices. Within a year, they’ve already developed an intuition for natural physics, can track objects even when they become partially or completely blocked, and can associate sounds with specific meanings. And by early childhood, they have a sophisticated understanding of grammar and thousands of words in their vocabularies.
For decades, we’ve dreamed of building intelligent machines with brains like ours — robotic assistants to clean our homes, cars that drive themselves, microscopes that automatically detect diseases. But building these artificially intelligent machines requires us to solve some of the most complex computational problems we have ever grappled with, problems that our brains can already solve in a manner of microseconds. To tackle these problems, we’ll have to develop a radically different way of programming a computer using techniques largely developed over the past decade. This is an extremely active field of artificial computer intelligence often referred to as deep learning. Read more…
A case study for mixing different data models within the same data store.
In recent years, the idea of “polyglot persistence” has emerged and become popular — for example, see Martin Fowler’s excellent blog post. Fowler’s basic idea can be interpreted that it is beneficial to use a variety of appropriate data models for different parts of the persistence layer of larger software architectures. According to this, one would, for example, use a relational database to persist structured, tabular data; a document store for unstructured, object-like data; a key/value store for a hash table; and a graph database for highly linked referential data. Traditionally, this means that one has to use multiple databases in the same project, which leads to some operational friction (more complicated deployment, more frequent upgrades) as well as data consistency and duplication issues.
This is the calamity that a multi-model database addresses. You can solve this problem by using a multi-model database that consists of a document store (JSON documents), a key/value store, and a graph database, all in one database engine and with a unifying query language and API that cover all three data models and even allow for mixing them in a single query. Without getting into too much technical detail, these three data models are specially chosen because an architecture like this can successfully compete with more specialised solutions on their own turf, both with respect to query performance and memory usage. The column-oriented data model has, for example, been left out intentionally. Nevertheless, this combination allows you — to a certain extent — to follow the polyglot persistence approach without the need for multiple data stores. Read more…
Integrated data stream platforms are poised to supplant the lambda architecture.
Data generation is growing exponentially, as is the demand for real-time analytics over fast input data. Traditional approaches to analyzing data in batch mode overcome the computational problems of data volume by scaling horizontally using a distributed system like Apache Hadoop. However, this solution is not feasible for analyzing large data streams in real time due to the scheduling I/O overhead it introduces.
Two main problems occur when batch processing is applied to stream or fast data. First, by the time the analysis is complete, it may already have been outdated by new incoming data. Second, the data may be arriving so fast that it is not feasible to store and batch-process them later, so the data must be processed or summarized when it is received. The Square Kilometer Array (SKA) radio telescope is a good public example of a system in which data must be preprocessed before storage. The SKA is a distributed radio observation project where each base station will receive 10-30 TB/sec and the Central Unit will process 4PB/sec. In this scenario, online summaries of the input data must be computed in real time and then processed — and significantly reduced in size — data is what’s stored.
In the business world, common examples of stream data are sensor networks, Twitter, Internet traffic, logs, financial tickers, click streams, and online bids. Algorithmic solutions enable the computation of summaries, frequency (heavy hitter) and event detection, and other statistical calculations on the stream as a whole or detection of outliers within it.
But what if you need to perform transaction-level analysis — scans across different dimensions of the data set, for example — as well as store the streamed data for fast lookup and retrospective analysis? Read more…
A new report explores how to evaluate your machine learning models.
Get notified when our free report “Evaluating Machine Learning Models: A beginner’s guide to key concepts and pitfalls” is available for download. Editor’s note: This is an excerpt of “Evaluating Machine Learning Models,” by Alice Zheng.
Alice Zheng will be part of the Data Science Summit and Dato Conference in July — a non-profit event jointly organized by Intel, Comcast, Pandora, Dato, Cloudera, and O’Reilly Media — in San Francisco. Visit the conference website for more information on the program. Use the discount code OREILLY20 and get 20% off either one or both days of the conference.
This report on evaluating machine learning models arose out of a sense of need. The content was first published as a series of six technical posts on the Dato Machine Learning Blog. I was the editor of the blog, and I needed something to publish for the next day. Dato builds machine learning tools that help users build intelligent data products. In our conversations with the community, we sometimes ran into a confusion in terminology. For example, people would ask for cross validation as a feature, when what they really meant was hyperparameter tuning, a feature we already had. So, I thought, “Aha! I’ll just quickly explain what these concepts mean and point folks to the relevant sections in the user guide.”
I sat down to write a blog post to explain cross validation, hold-out data sets, and hyperparameter tuning. After the first two paragraphs, however, I realized that it would take a lot more than a single blog post. The three terms sit at different depths in the concept hierarchy of machine learning model evaluation. Cross validation and hold-out validation are ways of chopping up a data set in order to measure the model’s performance on “unseen” data. Hyperparameter tuning, on the other hand, is a more “meta” process of model selection. But why does the model need “unseen” data, and what’s meta about hyperparameters? In order to explain all of that, I needed to start from the basics. First, I needed to explain the high-level concepts and how they fit together. Only then could I dive into each one in detail. Read more…
Oil and gas exploration have long been at the forefront of data collection and analysis.
Download our new free report, “Oil, Gas, and Data: High-Performance Data Tools in the Production of Industrial Power,” looking at the role of data, machine learning, and predictive analytics in oil and gas exploration.Petroleum extraction is an industry marked by price volatility and high capital exposure in new ventures. Big data is reducing risk, not just to capital, but to workers and the environment as well, as Dan Cowles explores in the new free report Oil, Gas, and Data.
At the Global Petroleum Show in Calgary, exhibiting alongside massive drill heads, chemical analysts, and the latest in valves and pipes are companies with a decidedly more virtual product: data. IBM’s Aspera, Abacus Datagraphics, Fujitsu, and Oracle’s Front Porch Digital are pitching data intake, analysis, and storage services to the oil industry, and industry stalwarts such as Halliburton, Lockheed Martin, and BP have been developing these capacities in-house.
The primary benefits of big data occur at the upstream end of petroleum production: exploration, discovery, and drilling. Better analysis of seismic and other geological data allows for drilling in more productive locations, and continual monitoring of equipment results in more uptime and better safety for both workers and environment. These marginal gains can be enough to keep an entire region competitive: the trio of cheap sensors, fast networks, and distributed computation that we’ve so often seen in other industries is the difference-maker keeping the North Sea oilfields productive in sub-$100/barrel market. Read more…
The O'Reilly Data Show Podcast: Ihab Ilyas on building data wrangling and data enrichment tools in academia and industry.
As I’ve written in previous posts, data preparation and data enrichment are exciting areas for entrepreneurs, investors, and researchers. Startups like Trifacta, Tamr, Paxata, Alteryx, and CrowdFlower continue to innovate and attract enterprise customers. I’ve also noticed that companies — that don’t specialize in these areas — are increasingly eager to highlight data preparation capabilities in their products and services.
During a recent episode of the O’Reilly Data Show Podcast, I spoke with Ihab Ilyas, professor at the University of Waterloo and co-founder of Tamr. We discussed how he started working on data cleaning tools, academic database research, and training computer science students for positions in industry.
Academic database research in data preparation
Given the importance of data integrity, it’s no surprise that the database research community has long been interested in data preparation and data wrangling. Ilyas explained how his work in probabilistic databases led to research projects in data cleaning:
In the database theory community, these problems of handling, dealing with data inconsistency, and consistent query answering have been a celebrated area of research. However, it has been also difficult to communicate these results to industry. And database practitioners, if you like, they were more into the well-structured data and assuming a lot of good properties around this data, [and they were also] more interested in indexing this data, storing it, moving it from one place to another. And now, dealing with this large amount of diverse heterogeneous data with tons of errors, sidled across all business units in the same enterprise became a necessity. You cannot really avoid that anymore. And that triggered a new line of research for pragmatic ways of doing data cleaning and integration. … The acquisition layer in that stack has to deal with large sets of formats and sources. And you will hear about things like adapters and source adapters. And it became a market on its own, how to get access and tap into these sources, because these are kind of the long tail of data.
The way I came into this subject was also funny because we were talking about the subject called probabilistic databases and how to deal with data uncertainty. And that morphed into trying to find data sets that have uncertainty. And then we were shocked by how dirty the data is and how data cleaning is a task that’s worth looking at.
See, extract, and create value with networks.
Networks of all kinds drive the modern world. You can build a network from nearly any kind of data set, which is probably why network structures characterize some aspects of most phenomenon. And yet, many people can’t see the networks underlying different systems. In this post, we’re going to survey a series of networks that model different systems in order to understand different ways networks help us understand the world around us.
We’ll explore how to see, extract, and create value with networks. We’ll look at four examples where I used networks to model different phenomenon, starting with startup ecosystems and ending in network-driven marketing.
Networks and markets
Commerce is one person or company selling to another, which is inherently a network phenomenon. Analyzing networks in markets can help us understand how market economies operate.
Strength of weak ties
The O'Reilly Radar Podcast: Turing Award winner Michael Stonebraker on the future of data science.
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In March 2015, database pioneer Michael Stonebraker was awarded the 2014 ACM Turing Award “for fundamental contributions to the concepts and practices underlying modern database systems.” In this week’s Radar Podcast, O’Reilly’s Mike Hendrickson sits down with Stonebraker to talk about winning the award, the future of data science, and the importance — and difficulty — of data curation.
One size does not fit all
Stonebraker notes that since about 2000, everyone has realized they need a database system, across markets and across industries. “Now, it’s everybody who’s got a big data problem,” he says. “The business data processing solution simply doesn’t fit all of these other marketplaces.” Stonebraker talks about the future of data science — and data scientists — and the tools and skill sets that are going to be required:
It’s all going to move to data science as soon as enough data scientists get trained by our universities to do this stuff. It’s fairly clear to me that you’re probably not going to retread a business analyst to be a data scientist because you’ve got to know statistics, you’ve got to know machine learning. You’ve got to know what regression means, what Naïve Bayes means, what k-Nearest Neighbors means. It’s all statistics.
All of that stuff turns out to be defined on arrays. It’s not defined on tables. The tools of future data scientists are going to be array-based tools. Those may live on top of relational database systems. They may live on top of an array database system, or perhaps something else. It’s completely open.
How big data, fast data, and real-time analytics work together in the real world.
Today, we often hear the phrase “The 3 Vs” in relation to big data: Volume, Variety and Velocity. With the interest and popularity of big data frameworks such as Hadoop, the focus has mostly centered on volume and data at rest. Common requirements here would be data ingestion, batch processing, and distributed queries. These are well understood. Increasingly, however, there is a need to manage and process data as it arrives, in real time. There may be great value in the immediacy of that data and the ability to act upon it very quickly. This is velocity and data in motion, also known as “fast data.” Fast data has become increasingly important within the past few years due to the growth in endpoints that now stream data in real time.
Big data + fast data is a powerful combination. However, adding real-time analytics to this mix provides the business value. Let’s look at a real example, originally described by Scott Jarr of VoltDB.
Consider a company that builds systems to manage physical assets in precious metal mines. Inside a mine, there are sensors on miners as well as shovels and other assets. For a lost shovel, minutes or hours of reporting latency may be acceptable. However, a sensor on a miner indicating a stopped heart should require immediate attention. The system should, therefore, be able to receive very fast data. Read more…
The O'Reilly Data Show Podcast: Phil Liu on the evolution of metric monitoring tools and cloud computing.
One of the main sources of real-time data processing tools is IT operations. In fact, a previous post I wrote on the re-emergence of real-time, was to a large extent prompted by my discussions with engineers and entrepreneurs building monitoring tools for IT operations. In many ways, data centers are perfect laboratories in that they are controlled environments managed by teams willing to instrument devices and software, and monitor fine-grain metrics.
During a recent episode of the O’Reilly Data Show Podcast, I caught up with Phil Liu, co-founder and CTO of SignalFx, a SF Bay Area startup focused on building self-service monitoring tools for time series. We discussed hiring and building teams in the age of cloud computing, building tools for monitoring large numbers of time series, and lessons he’s learned from managing teams at leading technology companies.
Evolution of monitoring tools
Having worked at LoudCloud, Opsware, and Facebook, Liu has seen first hand the evolution of real-time monitoring tools and platforms. Liu described how he has watched the number of metrics grow, to volumes that require large compute clusters:
One of the first services I worked on at LoudCloud was a service called MyLoudCloud. Essentially that was a monitoring portal for all LoudCloud customers. At the time, [the way] we thought about monitoring was still in a per-instance-oriented monitoring system. [Later], I was one of the first engineers on the operational side of Facebook and eventually became part of the infrastructure team at Facebook. When I joined, Facebook basically was using a collection of open source software for monitoring and configuration, so these are things that everybody knows — Nagios, Ganglia. It started out basically using just per-instance instant monitoring techniques, basically the same techniques that we used back at LoudCloud, but interestingly and very quickly as Facebook grew, this per-instance-oriented monitoring no longer worked because we went from tens or thousands of servers to hundreds of thousands of servers, from tens of services to hundreds and thousands of services internally.