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!

Why HPC Storage Matters More Now Than Ever: Analyst Q&A

September 17, 2021

With soaring data volumes and insatiable computing driving nearly every facet of economic, social and scientific progress, data storage is seizing the spotlight. Hyperion Research analyst and noted storage expert Mark No Read more…

GigaIO Gets $14.7M in Series B Funding to Expand Its Composable Fabric Technology to Customers

September 16, 2021

Just before the COVID-19 pandemic began in March 2020, GigaIO introduced its Universal Composable Fabric technology, which allows enterprises to bring together any HPC and AI resources and integrate them with networking, Read more…

What’s New in HPC Research: Solar Power, ExaWorks, Optane & More

September 16, 2021

In this regular feature, HPCwire highlights newly published research in the high-performance computing community and related domains. From parallel programming to exascale to quantum computing, the details are here. Read more…

Cerebras Brings Its Wafer-Scale Engine AI System to the Cloud

September 16, 2021

Five months ago, when Cerebras Systems debuted its second-generation wafer-scale silicon system (CS-2), co-founder and CEO Andrew Feldman hinted of the company’s coming cloud plans, and now those plans have come to fruition. Today, Cerebras and Cirrascale Cloud Services are launching... Read more…

AI Hardware Summit: Panel on Memory Looks Forward

September 15, 2021

What will system memory look like in five years? Good question. While Monday's panel, Designing AI Super-Chips at the Speed of Memory, at the AI Hardware Summit, tackled several topics, the panelists also took a brief glimpse into the future. Unlike compute, storage and networking, which... Read more…

AWS Solution Channel

Supporting Climate Model Simulations to Accelerate Climate Science

The Amazon Sustainability Data Initiative (ASDI), AWS is donating cloud resources, technical support, and access to scalable infrastructure and fast networking providing high performance computing (HPC) solutions to support simulations of near-term climate using the National Center for Atmospheric Research (NCAR) Community Earth System Model Version 2 (CESM2) and its Whole Atmosphere Community Climate Model (WACCM). Read more…

ECMWF Opens Bologna Datacenter in Preparation for Atos Supercomputer

September 14, 2021

In January 2020, the European Centre for Medium-Range Weather Forecasts (ECMWF) – a juggernaut in the weather forecasting scene – signed a four-year, $89-million contract with European tech firm Atos to quintuple its supercomputing capacity. With the deal approaching the two-year mark, ECMWF... Read more…

Why HPC Storage Matters More Now Than Ever: Analyst Q&A

September 17, 2021

With soaring data volumes and insatiable computing driving nearly every facet of economic, social and scientific progress, data storage is seizing the spotlight Read more…

Cerebras Brings Its Wafer-Scale Engine AI System to the Cloud

September 16, 2021

Five months ago, when Cerebras Systems debuted its second-generation wafer-scale silicon system (CS-2), co-founder and CEO Andrew Feldman hinted of the company’s coming cloud plans, and now those plans have come to fruition. Today, Cerebras and Cirrascale Cloud Services are launching... Read more…

AI Hardware Summit: Panel on Memory Looks Forward

September 15, 2021

What will system memory look like in five years? Good question. While Monday's panel, Designing AI Super-Chips at the Speed of Memory, at the AI Hardware Summit, tackled several topics, the panelists also took a brief glimpse into the future. Unlike compute, storage and networking, which... Read more…

ECMWF Opens Bologna Datacenter in Preparation for Atos Supercomputer

September 14, 2021

In January 2020, the European Centre for Medium-Range Weather Forecasts (ECMWF) – a juggernaut in the weather forecasting scene – signed a four-year, $89-million contract with European tech firm Atos to quintuple its supercomputing capacity. With the deal approaching the two-year mark, ECMWF... Read more…

Quantum Computer Market Headed to $830M in 2024

September 13, 2021

What is one to make of the quantum computing market? Energized (lots of funding) but still chaotic and advancing in unpredictable ways (e.g. competing qubit tec Read more…

Amazon, NCAR, SilverLining Team for Unprecedented Cloud Climate Simulations

September 10, 2021

Earth’s climate is, to put it mildly, not in a good place. In the wake of a damning report from the Intergovernmental Panel on Climate Change (IPCC), scientis Read more…

After Roadblocks and Renewals, EuroHPC Targets a Bigger, Quantum Future

September 9, 2021

The EuroHPC Joint Undertaking (JU) was formalized in 2018, beginning a new era of European supercomputing that began to bear fruit this year with the launch of several of the first EuroHPC systems. The undertaking, however, has not been without its speed bumps, and the Union faces an uphill... Read more…

How Argonne Is Preparing for Exascale in 2022

September 8, 2021

Additional details came to light on Argonne National Laboratory’s preparation for the 2022 Aurora exascale-class supercomputer, during the HPC User Forum, held virtually this week on account of pandemic. Exascale Computing Project director Doug Kothe reviewed some of the 'early exascale hardware' at Argonne, Oak Ridge and NERSC (Perlmutter), while Ti Leggett, Deputy Project Director & Deputy Director... Read more…

Ahead of ‘Dojo,’ Tesla Reveals Its Massive Precursor Supercomputer

June 22, 2021

In spring 2019, Tesla made cryptic reference to a project called Dojo, a “super-powerful training computer” for video data processing. Then, in summer 2020, Tesla CEO Elon Musk tweeted: “Tesla is developing a [neural network] training computer called Dojo to process truly vast amounts of video data. It’s a beast! … A truly useful exaflop at de facto FP32.” Read more…

Berkeley Lab Debuts Perlmutter, World’s Fastest AI Supercomputer

May 27, 2021

A ribbon-cutting ceremony held virtually at Berkeley Lab's National Energy Research Scientific Computing Center (NERSC) today marked the official launch of Perlmutter – aka NERSC-9 – the GPU-accelerated supercomputer built by HPE in partnership with Nvidia and AMD. Read more…

Google Launches TPU v4 AI Chips

May 20, 2021

Google CEO Sundar Pichai spoke for only one minute and 42 seconds about the company’s latest TPU v4 Tensor Processing Units during his keynote at the Google I Read more…

Esperanto, Silicon in Hand, Champions the Efficiency of Its 1,092-Core RISC-V Chip

August 27, 2021

Esperanto Technologies made waves last December when it announced ET-SoC-1, a new RISC-V-based chip aimed at machine learning that packed nearly 1,100 cores onto a package small enough to fit six times over on a single PCIe card. Now, Esperanto is back, silicon in-hand and taking aim... Read more…

Enter Dojo: Tesla Reveals Design for Modular Supercomputer & D1 Chip

August 20, 2021

Two months ago, Tesla revealed a massive GPU cluster that it said was “roughly the number five supercomputer in the world,” and which was just a precursor to Tesla’s real supercomputing moonshot: the long-rumored, little-detailed Dojo system. “We’ve been scaling our neural network training compute dramatically over the last few years,” said Milan Kovac, Tesla’s director of autopilot engineering. Read more…

CentOS Replacement Rocky Linux Is Now in GA and Under Independent Control

June 21, 2021

The Rocky Enterprise Software Foundation (RESF) is announcing the general availability of Rocky Linux, release 8.4, designed as a drop-in replacement for the soon-to-be discontinued CentOS. The GA release is launching six-and-a-half months after Red Hat deprecated its support for the widely popular, free CentOS server operating system. The Rocky Linux development effort... Read more…

Intel Completes LLVM Adoption; Will End Updates to Classic C/C++ Compilers in Future

August 10, 2021

Intel reported in a blog this week that its adoption of the open source LLVM architecture for Intel’s C/C++ compiler is complete. The transition is part of In Read more…

Iran Gains HPC Capabilities with Launch of ‘Simorgh’ Supercomputer

May 18, 2021

Iran is said to be developing domestic supercomputing technology to advance the processing of scientific, economic, political and military data, and to strengthen the nation’s position in the age of AI and big data. On Sunday, Iran unveiled the Simorgh supercomputer, which will deliver.... Read more…

Leading Solution Providers

Contributors

AMD-Xilinx Deal Gains UK, EU Approvals — China’s Decision Still Pending

July 1, 2021

AMD’s planned acquisition of FPGA maker Xilinx is now in the hands of Chinese regulators after needed antitrust approvals for the $35 billion deal were receiv Read more…

Hot Chips: Here Come the DPUs and IPUs from Arm, Nvidia and Intel

August 25, 2021

The emergence of data processing units (DPU) and infrastructure processing units (IPU) as potentially important pieces in cloud and datacenter architectures was Read more…

Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?

January 13, 2021

The rapid adoption of Julia, the open source, high level programing language with roots at MIT, shows no sign of slowing according to data from Julialang.org. I Read more…

10nm, 7nm, 5nm…. Should the Chip Nanometer Metric Be Replaced?

June 1, 2020

The biggest cool factor in server chips is the nanometer. AMD beating Intel to a CPU built on a 7nm process node* – with 5nm and 3nm on the way – has been i Read more…

HPE Wins $2B GreenLake HPC-as-a-Service Deal with NSA

September 1, 2021

In the heated, oft-contentious, government IT space, HPE has won a massive $2 billion contract to provide HPC and AI services to the United States’ National Security Agency (NSA). Following on the heels of the now-canceled $10 billion JEDI contract (reissued as JWCC) and a $10 billion... Read more…

Intel Launches 10nm ‘Ice Lake’ Datacenter CPU with Up to 40 Cores

April 6, 2021

The wait is over. Today Intel officially launched its 10nm datacenter CPU, the third-generation Intel Xeon Scalable processor, codenamed Ice Lake. With up to 40 Read more…

Quantum Roundup: IBM, Rigetti, Phasecraft, Oxford QC, China, and More

July 13, 2021

IBM yesterday announced a proof for a quantum ML algorithm. A week ago, it unveiled a new topology for its quantum processors. Last Friday, the Technical Univer Read more…

Frontier to Meet 20MW Exascale Power Target Set by DARPA in 2008

July 14, 2021

After more than a decade of planning, the United States’ first exascale computer, Frontier, is set to arrive at Oak Ridge National Laboratory (ORNL) later this year. Crossing this “1,000x” horizon required overcoming four major challenges: power demand, reliability, extreme parallelism and data movement. Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire