The Year in Machine Learning (Part Three)
This is the third installment in a four-part review of 2016 in machine learning and deep learning. In Part One, I covered Top Trends in the field, including concerns about bias, interpretability, deep learning’s explosive growth, the democratization of supercomputing, and the emergence of cloud machine learning platforms. In Part Two, I surveyed significant developments in Open Source machine learning projects, such as R, Python, Spark, Flink, H2O, TensorFlow, and others.
In this installment, we will review the machine learning and deep learning initiatives of Big Tech Brands — industry leaders with big budgets for software development and marketing. Big Tech Brands fall into three groups:
— SAS is the software revenue leader in predictive analytics. It has a unique business model and falls into its own category.
— Companies such as IBM, Microsoft, Oracle, SAP, and Teradata have all have strong franchises in the data warehousing market, and all except Teradata offer widely used business intelligence software. These companies have the financial strength to develop, market and cross-sell machine learning software to their existing customer base, and can impact the market if they choose to do so.
— Dell and HPE dabbled in advanced analytics and exited the market in 2016.
I covered Google and Amazon Web Services in Part One. Although neither company has a strong position in business analytics at present, they are making moves in that direction. Google set up Google Cloud Machine Learning as a distinct product group this year to service that market, and Amazon introduced QuickSight, a business analytics service.
Regular readers know that I favor open source software — as do most data scientists. Among the companies covered in this installment, IBM and Microsoft are making substantial commitments to the open source model, including direct contributions to open source software projects. They deserve kudos for that. Teradata is investing in Presto SQL, for which they get polite applause. Oracle and SAP leverage open source software in their solutions but make no significant contributions. SAS embraces open source the way a cat embraces a porcupine.
In Part Four, I will survey machine learning startups, and deliver results from the Bottom Story of the Year poll.
SAS leads the market in licensing revenue for advanced and predictive analytics software, according to IDC. The company has a loyal following among statisticians, actuaries, life scientists and others whose work depends on statistical analysis.
Partnering with IBM, SAS built its business in the 1970s on the strength of its software for the IBM System/360 mainframe. IBM promoted the software to its enterprise customers to increase adoption and use of its hardware. SAS software still runs on the mainframe, and the company continues to earn a significant share of its revenue on that platform. IBM has mainframe customers who use the big box exclusively for SAS.
In the 1990s, SAS successfully transitioned to a multi-vendor architecture and rebuilt its software to run on many different hardware platforms and operating systems. During this period, SAS established a reputation for industrial-strength and enterprise-grade software — in contrast to vendors like SPSS, who focused on building easy-to-use software for the desktop.
On the face of it, SAS has struggled to transition from server-based computing to the contemporary world of distributed architecture and cloud platforms. In the past ten years, the company has announced multiple initiatives to improve the performance and scalability of its products, with mixed success. In April, SAS announced Viya, its third attempt to deliver advanced analytics in a distributed MPP architecture.
What is SAS Viya? How does it differ from SAS’ previous attempts at high-performance design? Let’s peruse the brochure:
Cloud-ready, elastic and scalable
SAS Viya is built to be elastic and scalable for both private and public clouds. Analytical, in-memory computations are optimized for unconstrained environments, but they can also adjust for constrained environments. The elastic processing automatically adapts to needs and available resources – spinning up or winding down computing capacity as needed. Elastic scalability lets you quickly experiment with different scenarios and apply more complex approaches to larger amounts of streaming data.
Ahem. Any software is “cloud-ready,” in the sense that a Linux instance is a Linux instance whether it runs on-premises or in the cloud. And any software is elastic when you deploy it in a virtual appliance, such as an Amazon Machine Image. That includes SAS 9.4, which SAS touted as “cloud-ready” in 2014, and previous versions of SAS, which you could deploy in AWS even though SAS did not formally support the platform.
If you want to spin up software instances, however, you need software licenses. With open source software, such as Python, R, or Spark, that’s not an issue — you can spin up as many instances as you like without violating license agreements. Commercial software is more complicated since you need to pay for the licenses you want to spin up. Some vendors, like HPE and Teradata, tried to address this problem by marketing their own cloud platforms to compete with Amazon Web Services; they failed miserably. Others, like Oracle, partner with AWS to deliver their software in the cloud — either as a bundled managed service or on a “Bring Your Own License” (BYOL) model.
You can’t have elastic computing with commercial software without a flexible licensing model. Pay-for-what-you-use licensing poses a problem for vendors like SAS, because if customers only pay for what they use, they invariably pay a lot less than they do under term licensing. Most commercial software customers are over-licensed — they’re paying for a lot of software they don’t use. That is why revenue from on-premises software licensing is declining much faster than revenue from cloud-based subscriptions is rising. In the cloud, you can do more with less.
The bottom line is this: unless Viya is available under an elastic pricing model, nobody cares that it is “cloud-ready, elastic and scalable.”
If you want to have a little fun, the next time your SAS rep touts Viya’s elasticity, ask him what it will cost per hour to license the software. Watch him squirm.
Open analytics coding environment
Empower your data scientists with SAS Analytics that are easily available from a variety of programming languages. Whether it’s a Python notebook, Java client, Lua scripting interface or SAS, your modelers and data scientists can easily access the power of SAS for data manipulation, advanced analytics and analytical reporting.
We’ve all been waiting for the ability to run SAS from Lua.
Resilient architecture with guaranteed failover
For answers you depend on, you need analytical processing power you can count on. You need all your analytical computations to finish processing without interruption. The fault-tolerant design of SAS Viya automatically detects server failure, even in multiplatform processing environments, and redistributes processing as needed. It also manages several copies of data on the processing cluster. If a machine in the cluster becomes unavailable or fails, the required data is retrieved from another block to quickly continue processing. These self-healing mechanisms ensure high availability for uninterrupted processing and automated recovery.
“It runs on Hadoop.”
Interviewed in Forbes, SAS CEO Jim Goodnight speaks at length about Viya:
We are ready for big data…(we) just released our first version of our new Viya architecture, which is massively parallel computing where we spread the data out over dozens of servers and then use all the cores inside those servers to process the data in parallel. So we might have 500 cores working on the data all at once in parallel, and that allows it to handle some really, really big problems that we’ve never even thought of before. Things like logistic regression.
Someone should feed Dr. G. better talking points. Just for the record, commercially available software for logistic regression running in a massively parallel (MPP) environment first hit the market in 1989. Distributed logistic regression is currently available in multiple software packages, including one introduced by SAS five years ago.
Logistic regression (a non-linear model) is an iterative process. Essentially, you’re trying to estimate the parameters in the model, and so you take a guess, you’ve got to run through the data using that guess, then to refine it and do another guess and run through the data again, and you keep doing this over and over and over until the parameters converged or they don’t change much at all anymore. That can take 25 to 30 passes of the data. Now, in the old days, we used to have to read the data that many times. Now, it’s in memory. We put it in memory and it stays in memory. It’s spread out over 500 cores and then each one just does a little piece of the work, and so we can do those 25 iterations in just a few minutes, whereas it used to take hours.
It’s just like Spark, but with a license key.
(Viya’s) really our third generation of massively parallel computing. We’ve been working on this problem for seven years, and this is our third major crack at doing it, and this time we’ve got everything figured out.
In 2018 he’ll be talking about a fourth crack in nine years.
It’s possible that Viya works better than SAS’ previous cracks at high-performance analytics. That is a weak hurdle, however; SAS needs to demonstrate that its high-cost proprietary distributed framework is better than Apache Spark, which is rapidly emerging as the standard enterprise platform for Big Data.
While SAS supports machine learning techniques in several different products, it lags in deep learning. The SAS Marketing team created some helpful content about deep learning, but look carefully at that page — you won’t find an actual product for deep learning. Yes, I know that SAS Enterprise Miner supports multilayer perceptrons; but SAS does not support GPUs, Xeon Phi, Intel Nervana or any other high-performance architecture that will make it possible for you to train a deep neural net while you’re young.
If you think that an eighteen-year-old product running on one server is sufficient for your deep learning project, you should definitely talk to SAS. Keep in mind, though, that there is a reason that NVIDIA’s DGX-1 GPU-accelerated deep learning box has the power of 250 conventional servers: you actually need that kind of horsepower.
The rest of SAS’ business seems to be chugging along well enough. A combination of renewals, upgrades and upsells in existing accounts should produce low single-digit revenue growth for 2016, which is not a bad track record when you consider the declines reported by IBM, Oracle, and Teradata.
Business Analytics Leaders
The five companies in this group sell at least a billion dollars a year in business analytics software, according to IDC’s most recent worldwide software market share report. However, most of their revenue comes from data warehousing and business intelligence software; they all trail SAS in predictive analytics revenue.
Software licensing revenue is a misleading measure, however, due to the growing presence of open source software. IBM, Microsoft, and Oracle for example, actively use open source machine learning software to extend the reach of their data warehousing and business intelligence platforms, where they both have strong entries. IBM uses Spark as a foundation for many of its products; Microsoft has integrated R with SQL Server and PowerBI, and actively promotes the use of R for its enterprise customers. Oracle has taken a similar approach.
Unlike SAS, declining tech giant IBM never invested in a proprietary distributed framework for SPSS, its flagship software for advanced analytics. Instead, the company chose to leverage in-database engines (DB2, Netezza, and Oracle) and open source frameworks (MapReduce and Spark.)
IBM contributes to Apache Spark, which it uses in several products, and also to Apache SystemML. IBM Research developed the core of SystemML, which IBM donated to Apache in 2015. IBM has also visibly contributed to the Spark community through its efforts in education and training.
In 2016, IBM continued to market SPSS Statistics and SPSS Modeler, software brands it acquired in 2007. Release 18 of SPSS Modeler, announced in March, includes such things as support for machine learning in DB2 and support for IBM’s General Parallel File System (GPFS) in BigInsights. There aren’t too many data scientists who care about such things, but they appeal to the 150 or so enterprises with CIOs who still believe that nobody ever got fired for buying IBM.
In Part One of this review, I covered IBM’s machine learning moves in IBM Cloud, which I would characterize as Shakespearean, as in Much Ado About Nothing.
Microsoft had quite a year in machine learning and deep learning. As I noted in Parts One and Two, in 2016 MSFT launched cognitive APIs in Azure for vision, speech, language, knowledge, and search; a managed service for Spark in Azure HDInsight; enhancements to Azure Machine Learning and Version 2.0 of its deep learning framework, rebranded as Microsoft Cognitive Toolkit.
That’s just for starters.
In January, Microsoft announced Microsoft R Server, a rebranding of the product it acquired with Revolution Analytics in 2015. Microsoft R Server includes an enhanced R distribution, a scalable back-end, and integration tools. During the year, Microsoft two major releases for R Server. In Release 8, the company added push-down integration with Spark. Release 9 updated the Spark integration for Spark 2.0, and added MicrosoftML, a new R package for machine learning.
Microsoft announced SQL Server 2016 in March with embedded SQL Server R Services. On the Revolutions blog, David Smith reports on the launch. Tomaž Kaštrun explains what you can do with R services in SQL Server.
In November, after an extended preview, Microsoft announced the general availability of R Server for Azure HDInsight, a scale-out implementation of R integrated with Spark clusters created from HDInsight.
Also in Azure, Microsoft added a Linux version of the Data Science Virtual Machine (DSVM). Previously available as a Windows instance, DSVM includes Revolution R Open, Anaconda, Visual Studio Community Edition, PowerBI Desktop, SQL Server Express and the Azure SDK.
R Tools for Visual Studio launched to public preview in March, and to general availability in September. Also in September, Microsoft released the Microsoft R Client, a free data science tool that works with Microsoft R Open and the ScaleR distributed back end.
Microsoft data scientists Gopi Krishna Kumar, Hang Zhang and Jacob Spoelstra developed a methodology for data science, which they presented at the Microsoft Machine Learning and Data Science Summit 2016 in September. David Smith reports. The method, which the authors call Team Data Science Process, includes a standard directory structure for managing project artifacts using a system such as Git. It also includes open source utilities to support the process.
Other than that, it was a quiet year in Redmond.
Oracle has a surprisingly robust set of machine learning tools that appeal to Oracle-centric organizations. They include:
— Oracle Data Mining (ODM), a suite of machine learning algorithms that run as native SQL functions in Oracle Database.
— Oracle Data Miner, a client application for ODM with a business user interface.
— Oracle R Distribution (ORD), an enhanced free R distribution.
— Oracle R Enterprise (ORE), Oracle R Distribution packaged with tools to integrate R with Oracle Database.
— Oracle R Advanced Analytics for Hadoop (ORAAH), a set of R bindings with native algorithms and an interface to Spark.
Oracle claims that ORAAH’s native algorithms are faster than Spark, but ORAAH has only two algorithms, so nobody cares. Oracle OEMs Cloudera, so the Spark release is at least one major release behind the rest of the world.
Other than some dot releases for the components cited above, I don’t see a lot of movement for Oracle in 2016.
SAP introduced an update to its predictive analytics capabilities, now branded as SAP Business Objects Predictive Analytics 3.0. This product includes two separate automation capabilities, one branded as Predictive Factory, the second as HANA Automated Predictive Library. Predictive Factory, like SAS Factory Miner, is a scripting tool that enables a data scientist to create a modeling pipeline and schedules it for execution; it does not automate the data science process itself. HANA Automated Predictive Library is a set of functional calls that users can include in SQL scripts.
HANA Automated Predictive Library is a set of functional calls that users can include in SQL scripts. It’s a product that might appeal to SAP HANA bigots and nobody else.
SAP acquired KXEN and its InfiniteInsight software in 2014. Customer satisfaction promptly dropped through the floor, and SAP trails all other advanced analytics vendors rated in a Gartner survey. Legacy InfiniteInsight customers fall into two camps: (a) those whose IT organizations are heavily invested in SAP, and (b) everyone else. The former seem to be sticking with the software as SAP integrates it into its product line; the latter are heading for the exits.
Declining data warehouse vendor Teradata thinks of itself as an analytics powerhouse. In reality, most of its revenue comes from data warehousing, where the company gets high marks from analysts like Gartner.
You could say that Teradata has a commanding position at the bottom of the analytics stack.
Teradata’s executive leadership — if you can call it that — completely missed the implications of Hadoop and cloud computing. Instead, they bet that the Teradata brand was beloved by IT executives, who would keep on buying boxes in bulk. As a result of that blinkered view of the world, the company today is worth a third of what it was worth five years ago. Its product sales have declined for ten straight quarters, seven in a row at double digits.
After a dismal first quarter, Teradata’s board
fired accepted the resignation of CEO Mike Koehler; longtime board member Victor Lund stepped into the breach. In September, at the Teradata Partners conference, Lund announced that Teradata would reposition itself as an “analytics solutions” firm.
That may not sit well with SAS, Teradata’s primary partner for advanced analytics software, which also views itself as an “analytic solutions” firm. The difference, of course, is that SAS has been delivering solutions for a long time and has street cred with executives because it actually has sophisticated business solutions, with actual software and intellectual property, while Teradata appears to have little more than big ideas and PowerPoint.
Pro tip for Teradata management: just because you want to move up the value chain does not mean that you have the ability to do so.
In other developments, the company announced that Aster finally supports Spark, two years after anyone might have cared. Teradata also announced that Aster’s analytics are now available for deployment in Hadoop. Aster on Hadoop is a bladeless knife without a handle — a commercial machine learning library that competes with umpteen open source libraries. Aster also competes with another Teradata partner, Fuzzy Logix, whose dbLytix library is six times richer and more mature.
If someone proposes to bet that “solutions” and unbundled Aster will reverse Teradata’s decline, take the under.
Other Tech Giants
We mention two remaining giants, Dell and HPE, only to note their passing from the scene.
HPE announced the sale of its software assets (including Vertica and Haven) to U.K.-based Micro Focus for $2.5 billion in cash. Under terms of the deal, Micro Focus also granted equity with a soft valuation of $6.3 billion directly to HPE shareholders. HPE paid almost $20 billion over ten years for these assets. The valuation works out to about 2.4 times revenue, which means that both parties agree the business has little or no growth potential. Micro Focus has a reputation for
firing people cutting costs, so if you’re working for Haven or Vertica, this may be a good time to dust off your resume.
In March, HPE announced Haven OnDemand, available on Microsoft Azure. Haven is a
loose bundle of software assets salvaged from the train wreck of Autonomy, Vertica, ArcSight and HP Operations Management machine learning suite, initially branded as HAVEn and announced by HP in June 2013. In 2015, HP released Haven on Helion Public Cloud, HP’s failed cloud platform. So the March announcement is a re-re-release of the software.
Three years into its product life cycle, Haven hasn’t exactly caught on with data scientists. Just 2 out of 2,895 respondents to the KDnuggets 2016 Data Science Software Usage poll and none in the O’Reilly 2016 Data Science Salary Survey said they use the software. Adding insult to injury, Haven failed to make KDnuggets’ list of the top 50 machine learning APIs, a list that includes the likes of Ersatz, Hutoma, and Skyttle.
Vertica still has some traction with data lovers whose analysis needs are simple enough to satisfy with SQL. Currently, it’s the 28th most popular relational database, according to DB-Engines, which is about on par with Netezza and Greenplum and a lot better than Aster. Expect this ranking to drop like a stone in the hands of Micro Focus.
Goodbye, Dell. We hardly knew ye.