The O'Reilly Data Show Podcast: Carlos Guestrin on the early days of GraphLab and the evolution of GraphLab Create.
Editor’s note: Carlos Guestrin will be part of the team teaching Large-scale Machine Learning Day at Strata + Hadoop World in San Jose. Visit the Strata + Hadoop World website for more information on the program.
I only really started playing around with GraphLab when the companion project GraphChi came onto the scene. By then I’d heard from many avid users and admired how their user conference instantly became a popular San Francisco Bay Area data science event. For this podcast episode, I sat down with Carlos Guestrin, co-founder/CEO of Dato, a start-up launched by the creators of GraphLab. We talked about the early days of GraphLab, the evolution of GraphLab Create, and what’s he’s learned from starting a company.
MATLAB for graphs
Guestrin remains a professor of computer science at the University of Washington, and GraphLab originated when he was still a faculty member at Carnegie Mellon. GraphLab was built by avid MATLAB users who needed to do large scale graphical computations to demonstrate their research results. Guestrin shared some of the backstory:
“I was a professor at Carnegie Mellon for about eight years before I moved to Seattle. A couple of my students, Joey Gonzales and Yucheng Low were working on large scale distributed machine learning algorithms specially with things called graphical models. We tried to implement them to show off the theorems that we had proven. We tried to run those things on top of Hadoop and it was really slow. We ended up writing those algorithms on top of MPI which is a high performance computing library and it was just a pain. It took a long time and it was hard to reproduce the results and the impact it had on us is that writing papers became a pain. We wanted a system for my lab that allowed us to write more papers more quickly. That was the goal. In other words so they could implement this machine learning algorithms more easily, more quickly specifically on graph data which is what we focused on.”
We need primitives; pipeline synthesis tools; and most importantly, error analysis and verification.
Register for Hardcore Data Science Day at Strata + Hadoop World NYC 2015, which takes place September 29 to October 1.
There are many algorithms with implementations that scale to large data sets (this list includes matrix factorization, SVM, logistic regression, LASSO, and many others). In fact, machine learning experts are fond of pointing out: if you can pose your problem as a simple optimization problem then you’re almost done.
Of course, in practice, most machine learning projects can’t be reduced to simple optimization problems. Data scientists have to manage and maintain complex data projects, and the analytic problems they need to tackle usually involve specialized machine learning pipelines. Decisions at one stage affect things that happen downstream, so interactions between parts of a pipeline are an area of active research.
In his 2014 Strata + Hadoop World New York presentation, UC Berkeley professor Ben Recht described new UC Berkeley AMPLab projects for building and managing large-scale machine learning pipelines. Given AMPLab’s ties to the Spark community, some of the ideas from their projects are starting to appear in Apache Spark. Read more…
Drawing inspiration from recent advances in data preparation.
One of the trends we’re following is the rise of applications that combine big data, algorithms, and efficient user interfaces. As I noted in an earlier post, our interest stems from both consumer apps as well as tools that democratize data analysis. It’s no surprise that one of the areas where “cognitive augmentation” is playing out is in data preparation and curation. Data scientists continue to spend a lot of their time on data wrangling, and the increasing number of (public and internal) data sources paves the way for tools that can increase productivity in this critical area.
At Strata + Hadoop World New York, NY, two presentations from academic spinoff start-ups — Mike Stonebraker of Tamr and Joe Hellerstein and Sean Kandel of Trifacta — focused on data preparation and curation. While data wrangling is just one component of a data science pipeline, and granted we’re still in the early days of productivity tools in data science, some of the lessons these companies have learned extend beyond data preparation.
Scalability ~ data variety and size
Not only are enterprises faced with many data stores and spreadsheets, data scientists have many more (public and internal) data sources they want to incorporate. The absence of a global data model means integrating data silos, and data sources requires tools for consolidating schemas.
Random samples are great for working through the initial phases, particularly while you’re still familiarizing yourself with a new data set. Trifacta lets users work with samples while they’re developing data wrangling “scripts” that can be used on full data sets.
In this O'Reilly Data Show Podcast: Ion Stoica talks about the rise of Apache Spark and Apache Mesos.
Three projects from UC Berkeley’s AMPLab have been keenly adopted by industry: Apache Mesos, Apache Spark, and Tachyon. As an early user, it’s been fun to watch Spark go from an academic lab to the most active open source project in big data. In my recent travels, I’ve met Spark users from companies of all sizes and and from many industries. I’ve also spoken with companies that came of age before Spark was available or mature enough, and many are replacing homegrown tools with Spark (Full disclosure: I’m an advisor to Databricks, a start-up commercializing Apache Spark..)
A few months ago, I spoke with UC Berkeley Professor and Databricks CEO Ion Stoica about the early days of Spark and the Berkeley Data Analytics Stack. Ion noted that by the time his students began work on Spark and Mesos, his experience at his other start-up Conviva had already informed some of the design choices:
“Actually, this story started back in 2009, and it started with a different project, Mesos. So, this was a class project in a class I taught in the spring of 2009. And that was to build a cluster management system, to be able to support multiple cluster computing frameworks like Hadoop, at that time, MPI and others. To share the same cluster as the data in the cluster. Pretty soon after that, we thought about what to build on top of Mesos, and that was Spark. Initially, we wanted to demonstrate that it was actually easier to build a new framework from scratch on top of Mesos, and of course we wanted it to be also special. So, we targeted workloads for which Hadoop at that time was not good enough. Hadoop was targeting batch computation. So, we targeted interactive queries and iterative computation, like machine learning. Read more…
In this O'Reilly Data Show Podcast: Sarah Meiklejohn on analytic applications for blockchain and cryptocurrency technology.
Editor’s note: we’ll explore present and future applications of cryptocurrency and blockchain technologies at our upcoming Radar Summit: Bitcoin & the Blockchain on Jan. 27, 2015, in San Francisco.
A few data scientists are starting to play around with cryptocurrency data, and as bitcoin and related technologies start gaining traction, I expect more to wade in. As the space matures, there will be many interesting applications based on analytics over the transaction data produced by these technologies. The blockchain — the distributed ledger that contains all bitcoin transactions — is publicly available, and the underlying data set is of modest size. Data scientists can work with this data once it’s loaded into familiar data structures, but producing insights requires some domain knowledge and expertise.
I recently spoke with Sarah Meiklejohn, a lecturer at UCL, and an expert on computer security and cryptocurrencies. She was part of an academic research team that studied pseudo-anonymity (“pseudonymity”) in bitcoin. In particular, they used transaction data to compare “potential” anonymity to the “actual” anonymity achieved by users. A bitcoin user can use many different public keys, but careful research led to a few heuristics that allowed them to cluster addresses belonging to the same user:
“In theory, a user can go by many different pseudonyms. If that user is careful and keeps the activity of those different pseudonyms separate, completely distinct from one another, then they can really maintain a level of, maybe not anonymity, but again, cryptographically it’s called pseudo-anonymity. So, if they are a legitimate businessman on the one hand, they can use a certain set of pseudonyms for that activity, and then if they are dealing drugs on Silk Road, they might use a completely different set of pseudonyms for that, and you wouldn’t be able to tell that that’s the same user.
Andreas Antonopoulos urges the Canadian Senate to resist the temptation to centralize bitcoin.
Editor’s note: our O’Reilly Radar Summit: Bitcoin & the Blockchain will take place on January 27, 2015, at Fort Mason in San Francisco. Andreas Antonopoulos, Vitalik Buterin, Naval Ravikant, and Bill Janeway are but a few of the confirmed speakers for the event. Learn more about the event and reserve your ticket here.
We recently announced a Radar summit on present and future applications of cryptocurrencies and blockchain technologies. In a webcast presentation one of our program chairs, Kieren James-Lubin, observed that we’re very much in the early days of these technologies. He also noted that the technologies are complex enough that most users will rely on service providers (like wallets) to securely store, transfer, and receive cryptocurrencies.
As some of these service providers reach a certain scale, they will start coming under the scrutiny of regulators. Certain tenets are likely to remain: currencies require continuous liquidity and large financial institutions need access to the lender of last resort.
There are also cultural norms that take time to change. Take the example of notaries, whose services seem amenable to being replaced by blockchain technologies. Such a wholesale change would entail adjusting rules and norms across localities, which means going up against the lobbying efforts of established incumbents.
One way to sway regulators and skeptics is to point out that the decentralized nature of the (bitcoin) blockchain can unlock innovation in financial services and other industries. Mastering Bitcoin author Andreas Antonopoulos did a masterful job highlighting this in his recent testimony before the Canadian Senate:
“Traditional models for financial payment networks and banking rely on centralized control in order to provide security. The architecture of a traditional financial network is built around a central authority, such as a clearinghouse. As a result, security and authority have to be vested in that central actor. The resulting security model looks like a series of concentric circles with very limited access to the center and increasing access as we move farther away from the center. However, even the most outermost circle cannot afford open access.
In this episode of the O'Reilly Data Show Podcast, Jay Kreps talks about data integration, event data, and the Internet of Things.
At the heart of big data platforms are robust data flows that connect diverse data sources. Over the past few years, a new set of (mostly open source) software components have become critical to tackling data integration problems at scale. By now, many people have heard of tools like Hadoop, Spark, and NoSQL databases, but there are a number of lesser-known components that are “hidden” beneath the surface.
In my conversations with data engineers tasked with building data platforms, one tool stands out: Apache Kafka, a distributed messaging system that originated from LinkedIn. It’s used to synchronize data between systems and has emerged as an important component in real-time analytics.
In my travels over the past year, I’ve met engineers across many industries who use Apache Kafka in production. A few months ago, I sat down with O’Reilly author and Radar contributor Jay Kreps, a highly regarded data engineer and former technical lead for Online Data Infrastructure at LinkedIn, and most recently CEO/co-founder of Confluent. Read more…
Introducing Bitcoin & the Blockchain: An O’Reilly Radar Summit
When the creators of bitcoin solved the “double spend” problem in a decentralized manner, they introduced techniques that have implications far beyond digital currency. Our newly announced one-day event — Bitcoin & the Blockchain: An O’Reilly Radar Summit — is in line with our tradition of highlighting applications of developments in computer science. Financial services have long relied on centralized solutions, so in many ways, products from this sector have become canonical examples of the developments we plan to cover over the next few months. But many problems that require an intermediary are being reexamined with techniques developed for bitcoin.
How do you get multiple parties in a transaction to trust each other without an intermediary? In the case of a digital currency like bitcoin, decentralization means reaching consensus over an insecure network. As Mastering Bitcoin author Andreas Antonopoulos noted in an earlier post, several innovations lie at the heart of what makes bitcoin disruptive:
“Bitcoin is a combination of several innovations, arranged in a novel way: a peer-to-peer network, a proof-of-work algorithm, a distributed timestamped accounting ledger, and an elliptic-curve cryptography and key infrastructure. Each of these parts is novel on its own, but the combination and specific arrangement was revolutionary for its time and is beginning to show up in more innovations outside bitcoin itself.”
Rajiv Maheswaran talks about the tools and techniques required to analyze new kinds of sports data.
Many data scientists are comfortable working with structured operational data and unstructured text. Newer techniques like deep learning have opened up data types like images, video, and audio.
Other common data sources are garnering attention. With the rise of mobile phones equipped with GPS, I’m meeting many more data scientists at start-ups and large companies who specialize in spatio-temporal pattern recognition. Analyzing “moving dots” requires specialized tools and techniques.
A few months ago, I sat down with Rajiv Maheswaran founder and CEO of Second Spectrum, a company that applies analytics to sports tracking data. Maheswaran talked about this new kind of data and the challenge of finding patterns:
“It’s interesting because it’s a new type of data problem. Everybody knows that big data machine learning has done a lot of stuff in structured data, in photos, in translation for language, but moving dots is a very new kind of data where you haven’t figured out the right feature set to be able to find patterns from. There’s no language of moving dots, at least not that computers understand. People understand it very well, but there’s no computational language of moving dots that are interacting. We wanted to build that up, mostly because data about moving dots is very, very new. It’s only in the last five years, between phones and GPS and new tracking technologies, that moving data has actually emerged.”