Yottamine Serves Up Predictive Analytics On-Demand

By Tiffany Trader

February 18, 2013

Predictive modeling is not new, but startup Yottamine Analytics is counting on the convenience and economics of cloud computing to make it a lot more popular. Its cloud-based predictive modeling solution combines the benefits of EC2 spin-up automation and large-scale program parallelism to provide predictive power by the hour for pennies a minute.

Yottamine specializes in software for predictive analytics, specifically what is often referred to as machine learning. The software examines a large number of examples and develops a model that can then look at new data and make conclusions about it.

Take a spam filter, for example. If you were building a spam filter with this kind of software, you would essentially submit to the software, in a specially encoded form, examples of email messages, ones that are spam and ones that are not spam.

The spam filter example is provided by Tim Negris, the vice president of sales and marketing for Yottamine. Negris has been in the data arena for some time; he was formally an executive at Oracle and IBM as well as a number of other startups.

“What the software does is actually quite brilliant,” he says. “It figures out a mathematical formula that given a new example where it doesn’t know whether it’s spam or not, it can predict whether it is or it isn’t.”

This kind of computation is extraordinarily compute-intensive, says Negris. There’s this gigantic matrix of binary values that requires very complex multi-dimensional mathematics. It burns through a lot of cycles.

This kind of machine learning has been around for a while, but it hasn’t made good economic sense because of the very large computational requirement. “Everyone says the same thing,”  says Negris, “‘This is a very promising type of technology, but the computer you would need to have in your datacenter to do it is sufficiently large to where you’d have a difficult time doing it.’ That’s where HPC in the cloud comes into play.”

Yottamine has teamed up with Amazon Web Services to provide machine learning on-demand. “Normally this kind of process requires a lot of trial and error guesswork on the computation side and a lot of hand-work on the deployment side, including setting up the cluster instances,” says Negris. “Configuring this by-hand is not impossible, but takes some time. We automate a lot of those processes so the data scientist can say ‘here’s my data, spin me up a cluster and let’s get a model built.'”

“Using Amazon definitely advances the potential for this kind of machine learning. There’s an interesting affinity between the cost economics of cloud computing and the way in which you use a computer for this kind of thing. The designer spends a couple days figuring out what they need and getting the data ready and then the model is built and you’re off and running until you need to revise the model or build a new model,” is how Negris puts it.

The Yottamine team has been working on finalizing pricing details and lining up customers in preparation for their official launch, which is scheduled for next week. Depending on the algorithm, pricing will be in the range of $10-$50 per node hour. Expect standard linear machine learning algorithms to have a lower price point than more complex Gaussian variants, which can process enormous amounts of information very quickly.

Negris explains their solutions enable very fast runs, meaning a job that would normally take six days with typical open source research software might take only six hours with Yottamine’s software.

“The time compression is considerable,” he says. “You can do a huge model in the space of five hours. For approximately $250, you have a model that two years ago would have required you to own a two-million dollar supercomputer.”

Next >> In the Beginning

Yottamine founder David Huang has been working in machine learning for many years. He started out as a research post doc making algorithms scalable. He finds machine learning very interesting. “It’s not like some problems in biology where data needs are lower and everything can fit into memory but computation needs are very high and take a long time. Machine learning can be both compute and data-intensive if you use a complex model, which is what gives you higher accuracy,” he says.

Huang recalls his own frustrations coming up with big ideas and being told by his supervisor that they were impractical because there wasn’t enough compute power. So three years ago, he started his own company. He sees a huge opportunity for data scientists to be able to predict a wide range of variables for problems that require a huge amount of computing power.

“To solve these problems, we need to be able to use a lot of computing power, and that is complicated. To transform a machine learning algorithm from a single-threaded design into a parallelized computing environment is very difficult,” Huang explains.

To help them overcome the constraints that he once faced, he has worked to bring highly parallel, cloud-based machine learning software to data scientists.

“Our clients are working data scientists who on a daily basis need to do models for a given application; it could be insurance, it could be stock market or finance, it could be digital advertising. The one thing in common is they want to do a model fast and they want to do a highly-accurate model. Such a model requires a lot of computing power, and that’s where the majority of our use cases are in,” he says.

There are many different kinds of machine learning algorithms, and some are more easy to parallelize than others, but most often the best and most desirable are the hardest to parallelize, notes Huang. He points to support vector machines (SVM), which are very robust and have good performance in terms of accuracy, but require a lot of computing power and are not easy to parallelize. “What we’re doing is taking one of the best algorithms out there and parallelizing it so it’s scalable and provides robust performance for data scientists,” he says.

The cloud poses its own challenges, though. There are cost and time considerations that go with moving data into and out of the cloud. The startup’s founder points out that data into Amazon is free; the cloud provider only charges for data out. On top of that, Yottamine doesn’t charge extra for their clients’ Amazon usage. The majority of users will use the Internet to upload their data to Amazon’s S3 storage system.

The models dictate the size of the data. Some are very compact, so getting them out of the cloud is not that expensive or time-consuming. But if someone had an enormous data set, they could use Amazon’s disk delivery system (aka sneakernet). However, the Yottamine reps do not anticipate many issues. “The data charges are small compared to the computational charges from an infrastructure cost standpoint,” observes Negris.

Regarding potential data concerns around security and privacy, the rep is quick to respond: “In our case, the data that’s actually being operated on – the data that’s being submitted to the SVM algorithm – is simply a binary matrix; it’s not a data set in any conventional sense. We stuff it into Hadoop, but it’s a binary matrix,” he says.

“Not only is it denatured, it goes beyond that. There’s a fundamental data transformation on the way to the cloud where what you’re handing up is a matrix of ones and zeros that has zero human-recognizable information or even machine-recognizable information, because the metadata is not necessary. Column heading information – ­ race, zip code or age, for example ­ – is not necessary to obtain the model. Somebody trying to get at the data would see a one in the first column, but wouldn’t know what that column was – there wouldn’t be a way to reverse engineer it.”

When it comes to Amazon EC2 instance types, Yottamine selects the virtual machine that best matches the problem size and the algorithm selected. The default for a linear algorithm that requires high CPU performance is the High-CPU Extra Large Instance. For nonlinear problems, which are more complex and require more memory even when the size of the problem is not very big, the company often employs Amazon’s High Memory Instance, specifically the High-Memory Quadruple Extra Large Instance.

Memory is key; in-memory computing achieves the biggest efficiencies. For the fastest possible configurations, Yottamine utilizes the EC2 High-I/O Instance which is backed by SSD for even more speed, but since this is still a newer instance type Amazon generally limits their use to two per user. The High-I/O Instance also includes 10 Gigabit Ethernet, so it’s a good fit for algorithms that rely on parallel programming models like MPI, where that high-speed interconnect counts most.

But speed isn’t always the ultimate priority for customers. Negris explains that it often depends on the vertical and the frequency at which the model is refreshed. The models used in credit card fraud detection do not refresh that often – perhaps on a quarterly basis. It’s a very rigid and well-established domain, and the information is well-defined. But the data coming from a click-stream could not be more opposite, so the Web advertising sector has high-turnover for model design. Thus they tend to be more speed-sensitive and less cost-sensitive.

As for likely competition, Negris divides potential challenges into two camps. He anticipates the most difficult market to crack will be current Oracle and SAS customers. These traditional software vendors offer an SVM algorithm as part of their portfolio. Yottamine, however, claims to offer a wider selection of predictive modeling software with six different SVM algorithms alone, which gives them the edge in some situations.

The other market penetration challenge comes from research algorithms that were initially built as part of an academic project, thesis or dissertation and were put into the open source stream, but Negris asserts that these aren’t very polished or industry-hardened since they were usually built for a single-purpose. They also require a certain amount of user expertise and experience.

Yottamine is hoping that its unique cloud-based approach and focus on performance, accuracy and easy of use will give its software the advantage over these conventional commercial and open source algorithms.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

Geospatial Data Research Leverages GPUs

August 17, 2017

MapD Technologies, the GPU-accelerated database specialist, said it is working with university researchers on leveraging graphics processors to advance geospatial analytics. The San Francisco-based company is collabor Read more…

By George Leopold

Intel, NERSC and University Partners Launch New Big Data Center

August 17, 2017

A collaboration between the Department of Energy’s National Energy Research Scientific Computing Center (NERSC), Intel and five Intel Parallel Computing Centers (IPCCs) has resulted in a new Big Data Center (BDC) that Read more…

By Linda Barney

Google Releases Deeplearn.js to Further Democratize Machine Learning

August 17, 2017

Spreading the use of machine learning tools is one of the goals of Google’s PAIR (People + AI Research) initiative, which was introduced in early July. Last week the cloud giant released deeplearn.js as part of that in Read more…

By John Russell

HPE Extreme Performance Solutions

Leveraging Deep Learning for Fraud Detection

Advancements in computing technologies and the expanding use of e-commerce platforms have dramatically increased the risk of fraud for financial services companies and their customers. Read more…

Spoiler Alert: Glimpse Next Week’s Solar Eclipse Via Simulation from TACC, SDSC, and NASA

August 17, 2017

Can’t wait to see next week’s solar eclipse? You can at least catch glimpses of what scientists expect it will look like. A team from Predictive Science Inc. (PSI), based in San Diego, working with Stampede2 at the Read more…

By John Russell

Microsoft Bolsters Azure With Cloud HPC Deal

August 15, 2017

Microsoft has acquired cloud computing software vendor Cycle Computing in a move designed to bring orchestration tools along with high-end computing access capabilities to the cloud. Terms of the acquisition were not disclosed. Read more…

By George Leopold

HPE Ships Supercomputer to Space Station, Final Destination Mars

August 14, 2017

With a manned mission to Mars on the horizon, the demand for space-based supercomputing is at hand. Today HPE and NASA sent the first off-the-shelf HPC system i Read more…

By Tiffany Trader

AMD EPYC Video Takes Aim at Intel’s Broadwell

August 14, 2017

Let the benchmarking begin. Last week, AMD posted a YouTube video in which one of its EPYC-based systems outperformed a ‘comparable’ Intel Broadwell-based s Read more…

By John Russell

Deep Learning Thrives in Cancer Moonshot

August 8, 2017

The U.S. War on Cancer, certainly a worthy cause, is a collection of programs stretching back more than 40 years and abiding under many banners. The latest is t Read more…

By John Russell

IBM Raises the Bar for Distributed Deep Learning

August 8, 2017

IBM is announcing today an enhancement to its PowerAI software platform aimed at facilitating the practical scaling of AI models on today’s fastest GPUs. Scal Read more…

By Tiffany Trader

IBM Storage Breakthrough Paves Way for 330TB Tape Cartridges

August 3, 2017

IBM announced yesterday a new record for magnetic tape storage that it says will keep tape storage density on a Moore's law-like path far into the next decade. Read more…

By Tiffany Trader

AMD Stuffs a Petaflops of Machine Intelligence into 20-Node Rack

August 1, 2017

With its Radeon “Vega” Instinct datacenter GPUs and EPYC “Naples” server chips entering the market this summer, AMD has positioned itself for a two-head Read more…

By Tiffany Trader

Cray Moves to Acquire the Seagate ClusterStor Line

July 28, 2017

This week Cray announced that it is picking up Seagate's ClusterStor HPC storage array business for an undisclosed sum. "In short we're effectively transitioning the bulk of the ClusterStor product line to Cray," said CEO Peter Ungaro. Read more…

By Tiffany Trader

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “ Read more…

By Tiffany Trader

Reinders: “AVX-512 May Be a Hidden Gem” in Intel Xeon Scalable Processors

June 29, 2017

Imagine if we could use vector processing on something other than just floating point problems.  Today, GPUs and CPUs work tirelessly to accelerate algorithms Read more…

By James Reinders

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Just how close real-wo Read more…

By John Russell

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurrencies like Bitcoin, along with classified government communications and other sensitive digital transfers. Read more…

By Doug Black

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its a Read more…

By Tiffany Trader

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the cam Read more…

By John Russell

Leading Solution Providers

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of Read more…

By Alex Woodie

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Engine (GCE) job. Sutherland ran the massive mathematics workload on 220,000 GCE cores using preemptible virtual machine instances. Read more…

By Tiffany Trader

Six Exascale PathForward Vendors Selected; DoE Providing $258M

June 15, 2017

The much-anticipated PathForward awards for hardware R&D in support of the Exascale Computing Project were announced today with six vendors selected – AMD Read more…

By John Russell

Top500 Results: Latest List Trends and What’s in Store

June 19, 2017

Greetings from Frankfurt and the 2017 International Supercomputing Conference where the latest Top500 list has just been revealed. Although there were no major Read more…

By Tiffany Trader

IBM Clears Path to 5nm with Silicon Nanosheets

June 5, 2017

Two years since announcing the industry’s first 7nm node test chip, IBM and its research alliance partners GlobalFoundries and Samsung have developed a proces Read more…

By Tiffany Trader

Messina Update: The US Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

  • arrow
  • Click Here for More Headlines
  • arrow
Share This