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In September 2018, IBM introduced a brand new product, IBM Db2 AI for z/OS. This artificial intelligence engine monitors facts access patterns from executing SQL statements, makes use of computing device studying algorithms to opt for greatest patterns and passes this suggestions to the Db2 query optimizer to be used by using subsequent statements.computing device gaining knowledge of on the IBM z Platform
In may additionally of 2018, IBM announced version 1.2 of its machine researching for z/OS (MLz) product. here's a hybrid zServer and cloud software suite that ingests efficiency facts, analyzes and builds models that represent the fitness popularity of a lot of warning signs, monitors them over time and offers precise-time scoring capabilities.
a few facets of this product providing are aimed toward supporting a group of mannequin builders and managers. for example:
This desktop getting to know suite became in the beginning aimed at zServer-based analytics applications. one of the vital first evident selections became zSystem efficiency monitoring and tuning. equipment administration Facility (SMF) records that are instantly generated through the working equipment give the raw facts for gadget useful resource consumption similar to central processor utilization, I/O processing, memory paging and so on. IBM MLz can assemble and shop these statistics over time, and construct and educate models of equipment conduct, ranking these behaviors, identify patterns not without difficulty foreseen by way of humans, improve key efficiency indications (KPIs) after which feed the mannequin outcomes back into the gadget to affect system configuration alterations that may enrich performance.
The subsequent step was to put into effect this suite to research Db2 efficiency records. One solution, known as the IBM Db2 IT Operational Analytics (Db2 ITOA) solution template, applies the machine researching know-how to Db2 operational information to benefit an figuring out of Db2 subsystem health. it will possibly dynamically build baselines for key efficiency symptoms, provide a dashboard of those KPIs and give operational body of workers real-time insight into Db2 operations.
while common Db2 subsystem efficiency is a crucial aspect in common utility health and efficiency, IBM estimates that the DBA support staff spends 25% or greater of its time, " ... combating access course issues which trigger performance degradation and repair impact.". (See Reference 1).AI comes to Db2
trust the plight of modern DBAs in a Db2 atmosphere. In cutting-edge IT world they should aid one or extra huge statistics functions, cloud application and database capabilities, software installation and configuration, Db2 subsystem and utility efficiency tuning, database definition and management, catastrophe healing planning, and more. question tuning has been in existence seeing that the origins of the database, and DBAs are usually tasked with this as neatly.
The heart of question route evaluation in Db2 is the Optimizer. It accepts SQL statements from purposes, verifies authority to entry the records, experiences the areas of the objects to be accessed and develops a listing of candidate information access paths. These entry paths can include indexes, desk scans, a number of desk be a part of methods and others. in the statistics warehouse and big records environments there are always extra decisions obtainable. One of these is the existence of summary tables (now and again known as materialized query tables) that comprise pre-summarized or aggregated statistics, therefore permitting Db2 to avoid re-aggregation processing. one more option is the starjoin access direction, regular within the information warehouse, where the order of desk joins is changed for efficiency reasons.
The Optimizer then reports the candidate entry paths and chooses the access path, "with the lowest cost." charge during this context potential a weighted summation of aid usage including CPU, I/O, memory and different elements. at last, the Optimizer takes the bottom charge entry path, retailers it in memory (and, optionally, in the Db2 directory) and begins access route execution.
large records and records warehouse operations now include utility suites that allow the company analyst to make use of a graphical interface to construct and manipulate a miniature information model of the statistics they want to analyze. The packages then generate SQL statements in response to the clients’ requests.
The difficulty for the DBA
so as to do respectable analytics in your varied records stores you need an excellent understanding of the records necessities, an knowing of the analytical capabilities and algorithms available and a excessive-efficiency facts infrastructure. regrettably, the number and site of data sources is expanding (each in measurement and in geography), statistics sizes are transforming into, and applications continue to proliferate in quantity and complexity. How should still IT managers support this atmosphere, above all with probably the most skilled and mature workforce nearing retirement?
keep in mind also that a large part of decreasing the whole cost of ownership of these systems is to get Db2 purposes to run sooner and extra effectively. This continually interprets into the usage of fewer CPU cycles, doing fewer I/Os and transporting less data throughout the community. considering that it's commonly complex to even establish which applications could advantage from performance tuning, one method is to automate the detection and correction of tuning considerations. this is the place laptop discovering and synthetic intelligence will also be used to splendid effect.Db2 12 for z/OS and artificial Intelligence
Db2 edition 12 on z/OS makes use of the computer learning amenities mentioned above to acquire and shop SQL query textual content and entry route details, as well as specific performance-linked historical information reminiscent of CPU time used, elapsed times and effect set sizes. This providing, described as Db2 AI for z/OS, analyzes and shops the information in laptop learning models, with the mannequin evaluation results then being scored and made obtainable to the Db2 Optimizer. The subsequent time a scored SQL statement is encountered, the Optimizer can then use the model scoring information as enter to its entry course choice algorithm.
The outcome should still be a reduction in CPU consumption because the Optimizer uses mannequin scoring enter to opt for more desirable entry paths. This then lowers CPU costs and speeds software response times. a big competencies is that the use of AI application does not require the DBA to have facts science talents or deep insights into query tuning methodologies. The Optimizer now chooses the most effective entry paths based not best on SQL question syntax and data distribution data however on modelled and scored old efficiency.
This can be above all crucial in case you save facts in distinctive areas. for example, many analytical queries against massive information require concurrent access to certain information warehouse tables. These tables are frequently known as dimension tables, and that they include the facts aspects continually used to manage subsetting and aggregation. as an instance, in a retail ambiance believe a table called StoreLocation that enumerates each keep and its region code. Queries in opposition t keep revenue information may additionally wish to mixture or summarize sales by means of location; hence, the StoreLocation desk should be used by some huge information queries. during this ambiance it's regular to take the dimension tables and duplicate them always to the massive records application. within the IBM world this vicinity is the IBM Db2 Analytics Accelerator (IDAA).
Now suppose about SQL queries from both operational functions, facts warehouse clients and big facts business analysts. From Db2's perspective, all these queries are equal, and are forwarded to the Optimizer. however, within the case of operational queries and warehouse queries they should definitely be directed to entry the StoreLocation desk within the warehouse. however, the query from the company analyst against big statistics tables should likely access the copy of the desk there. This effects in a proliferations of competencies access paths, and extra work for the Optimizer. luckily, Db2 AI for z/OS can provide the Optimizer the counsel it must make smart entry route decisions.the way it Works
The sequence of pursuits in Db2 AI for z/OS (See Reference 2) is generally here:
There are also a lot of user interfaces that provide the administrator visibility to the status of the accumulated SQL commentary efficiency records and mannequin scoring.summary
IBM's laptop researching for zOS (MLz) providing is being used to awesome impact in Db2 edition 12 to increase the efficiency of analytical queries in addition to operational queries and their associated applications. This requires management attention, as you ought to assess that your company is ready to devour these ML and AI conclusions. How will you measure the prices and advantages of the usage of desktop learning? Which IT assist body of workers should be tasked to reviewing the effect of model scoring, and maybe approving (or overriding) the results? How will you overview and justify the assumptions that the application makes about access path choices?
In different phrases, how neatly did you know your information, its distribution, its integrity and your latest and proposed access paths? this will determine where the DBAs spend their time in supporting analytics and operational software efficiency.
# # #
John Campbell, IBM Db2 exotic EngineerFrom "IBM Db2 AI for z/OS: boost IBM Db2 software efficiency with computer researching"https://www.worldofdb2.com/movements/ibm-db2-ai-for-z-os-increase-ibm-db2-utility-performance-with-ma
Db2 AI for z/OShttps://www.ibm.com/guide/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html
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The price of bitcoin (BTC-USD) has been normally falling seeing that Sept. 1, when it was $7,203, to $3,590 as of these days. The Ethereum (ETHB-USD) rate has lowered by using fifty seven% all the way through the same period. In November, Giga Watt (WTT-USD), one of the crucial 5 greatest mining agencies in the world, announced chapter. These events all element to the unhappy truth of the whole cryptocurrency market.
The condition has been exacerbated via recent information, even though now not confirmed, that in China mining device is being thrown into "mining dumps." interestingly, many small scale bitcoin mining farms file startling losses and are now promoting the hardware "with the aid of the pound." In December, a blogger nicknamed "BTCKING555" disclosed what turned into presupposed to be leaked financial statistics from Bitmain's Q3 fiscal record which confirmed the ASIC chip producer racking up a lack of $740 million.
Is the crypto trade dealing with a large scale disaster? To determine what's occurring, we are able to distinguish between the circumstance in the cryptocurrency market and the real scenario in the adoption of blockchain technology.latest news about blockchain adoption
New studies seem per week about the integration of the distributed ledger know-how (DLT) in quite a lot of niches of economy and numerous industries. In August, the biggest sea freight carrier Maersk in partnership with IBM launched a blockchain-based mostly assignment geared toward streamlining the transport of goods from Europe to the united states. American company gigantic Walmart (WMT) also started to cooperate with IBM (IBM) on a assignment for monitoring pork elements from China and the platform now extends to other styles of goods. Walmart has also developed its own blockchain-based mostly "wise packages" platform.
huge-scale transformations linked to using DLT are seen within the energy sector. initiatives aimed toward stimulating using sensible contracts in the distribution of electricity are being developed in Russia, Germany, Chile, terrific Britain, and Japan.Blockchain expertise on earth of finance
Blockchain has additionally attracted monetary agencies, as distributed registries guarantee the protection of users in opposition t fraud. returned in 2014, 9 of the world's greatest banks -- Barclays (BCS), credit score Suisse (CS), JPMorgan (JPM), UBS (UBS), Goldman Sachs (GS), and others -- agreed to create a monetary and technological enterprise R3CEV to strengthen blockchain technology purposes in the fiscal gadget. to date, more than 70 banks and economic companies from in all places the world have joined R3.
other agencies are also at present gaining knowledge of using DLT. for example, the largest inventory alternate in Switzerland -- SIX Swiss change -- announced the building of a blockchain-based platform for the tokenization of securities. on the state degree, blockchain is also regarded attractive. The relevant financial institution of Canada recommended using blockchain in operations with securities, and SWIFT is checking out the pass-border funds gateway by using DLT.Why does blockchain entice hobby on earth of massive funds?
The activity of the economic business is drawn to DLT because it presents a whole latitude of recent opportunities. first off, it considerations the safety of payments and banking guidance. on the grounds that the insurance plan of all the information stored in blockchain is ensured by cryptography, it is nearly impossible to change the tips concerning the funds made. DLT additionally eliminates blunders and screw ups when making funds within banks. flawed write-offs that clients of monetary companies commonly face these days are averted if the gadget is blockchain-powered.
the use of blockchain, banks could be able to evade reporting error this could greatly simplify their communique with supervisory authorities. additionally, the brand new expertise can significantly enhance the pace of processing payments, considering it eliminates the need for lengthy processes, coordination and confirmation. in line with the international consulting enterprise Accenture (ACN), the introduction of blockchain will permit banks to in the reduction of the cost of the leading monetary reporting through 70% as a result of optimization of statistics first-rate, transparency and inside manage. or not it's additionally pointed out that 30%-50% talents can charge mark downs on compliance can be carried out by way of expanding transparency and ease of cross-checking monetary transactions. The expenses of centralized activities, such as the consumer servicing, can be halved via improving digital identity mechanisms and simplifying the sharing of customer information for all individuals within the system.
based on Accenture analysts, using blockchain, banks can reduce ordinary fees via 27%-38%:
it be no surprise that spending on blockchain in the financial functions sector is accelerating, primarily at a time when the rising charge of capital is making it hard to boost profitability by means of traditional methods. ultimately, it might allow decommissioning of large constituents of present day returned office infrastructure and externalization of key operational methods to business utilities - profoundly impacting charge dynamics.Cryptocurrency exchangers and exchanges
The contemporary cryptocurrency trade is according to digital foreign money trading supported by using crypto exchanges and instant buying and selling marketplaces (exchangers). unluckily, these tools may additionally occur to be unreliable.
today, the lion's share of purchase, promote or alternate transactions involving digital money is performed through exchangers and exchanges. at the equal time, the company of the exchangers is built on the fact that it is cheaper to purchase Bitcoin and other cash from one customer, and then sell to others as expensive as viable. during this case the purchase and sale fee will depend on the exchanger itself.
towards this history, crypto exchanges seem like a mannequin of market justice as a result of right here transactions are made at once between the owners of crypto property. youngsters, the purposeful of the overwhelming majority of exchanges is not any different from exchangers.
Their chronic difficulty is low bandwidth -- no crypto-trade is yet in a position to process a big number of transactions characteristic of the usual finance market. moreover, every alternate operates on the foundation of its personal technical solutions, the reliability of which may well be questionable. $927 million stolen from cryptocurrency exchanges in 2018 only are the proof of this.
particularly, crypto exchanges and exchangers do not use blockchain technology. Most of them are centralized structures which are fully opaque to the individuals. This consequences in security issues -- from the theft of non-public facts to stealing money from customer wallets.
in fact, the contributors of the crypto market are forced to cope with an unreliable infrastructure, use opaque and prone technical options with very restrained performance, incomparable with the alternatives provided by way of usual banks.
These complications are smartly known to experts, for this reason new initiatives aimed at providing the crypto market members with all of the services latest in ordinary banks are developed and launched. Yet, banks, of their flip, also suffer from a number of chronic complications, similar to low price rates (foreign money transfers can take several days) and high expenses, due to the proven fact that a large number of intermediaries are concerned in such methods.
The chance to use blockchain technologies for fixing these complications has ended in the emergence of a couple of gigantic-scale, bold and interesting initiatives to create decentralized price programs that enable sending funds international in seconds at just about zero charge, in addition to greater reliably.New period of banks
The monstrous majority of makes an attempt to make use of blockchain in the economic sector to this point have concentrated best on one particular service. There comes a time for tasks geared toward combining the surest alternate options that average banks and blockchain technologies give.
In December 2017, a world digital cryptobank called Crypterium (CRPT-USD) was launched. The Estonia-based mission, which raised $52 million all over ICO, presents economic services to users that thus far most effective banks and different financial institutions supply, by using cryptocurrency as an alternative of fiat cash.
The task creators present customers the possibility to purchase and sell essentially the most universal cryptocurrencies, overseas cash transfers, cryptocurrency funds, loyalty programs and cashback, and the like. Led by means of former well-known supervisor of Visa (V), Crypterium currently provides a mobile app which allows for transacting cryptocurrency even without a need to have a special pockets -- recipients of funds want their cell number most effective.
In 2018, the TraDove (BBC-USD) venture launched the world's first cryptocurrency fee gadget which permits for transactions between legal entities and the cryptocurrency exchanger. The enterprise is at present constructing a straightforward, quick and authentic blockchain fee device for processing overseas transactions.
a much broader set of fiscal capabilities in response to blockchain is obtainable by means of the FOTON assignment, in which a decentralized digital crypto financial institution will turn into the core of a decentralized company platform, convenient for both organizations and people.
The bank promises its customers the opportunity to make transactions with fiat cash and cryptocurrency, make deposits or take loans in cryptocurrency, or just trade for fiat money. Claiming to be the bank of the long run, FOTON strives to give modern clients with all latitude of functions for buying and selling forex without intermediaries, which turns into viable with the developed-in cryptocurrency alternate. Digital funds can be transferred anyplace on the planet with well-nigh no volume limits, directly, with minimal charges and in a comfortable means.
The technologies used in such initiatives permit changing cryptocurrency for individuals using NFC technology, immediately processing transactions with each digital and fiat money, while guaranteeing their interchangeability.
moreover, clients of such options are capable of use cryptocurrency to pay for items with none restrictions and with a low commission on any of the 42 million existing price terminals on the planet, as well as in many on-line retailers and price centers. on the same time, cryptocurrency payments will become extra convenient, fast and cozy, together with the use of virtual debit and credit playing cards. The mission's founders promise that their techniques will permit purchasing a cup of espresso for cryptocurrency in barely two or three seconds.
Blockchain opens up a brand new standpoint on the planet of monetary agencies, enabling them to desert a huge number of inefficient methods and infrastructure aspects and defeat competitors through attracting clients with new alternatives -- from speedy, protected and affordable payments to clear and profitable lending. Such opportunities will continue to fuel hobby in disbursed ledger expertise, the study and enlargement of its capabilities.
The percentages of blockchain know-how are functionally limitless. If we allow it to achieve its authentic advantage, we will use it to transform the global financial system on practically every imaginable degree, and our societies along with it, says Sasha Pitkevich, Senior manager at Accenture know-how.what is anticipated in 2019?
In 2019, many solutions which are currently beneath development will beginning to be used within the commercial tactics of economic and IT corporations. In March, for example, the launch of the TON community examine community is anticipated from the world-standard messenger Telegram, which raised $1.7 billion in deepest funding rounds.
buyers looking for the chance to be a part of the blockchain and make funds on its implementation can also accept as true with the stocks of such colossal corporations as Microsoft (MSFT), IBM, bank of america (BAC), Overstock (OSTK), Walmart, which are actively experimenting during this path and are already featuring technical solutions to the world's biggest companies based on DLT. ultimately, this will doubtless boost their competitiveness. purchasing shares of such companies can also be a fine choice for buyers who are looking to capitalize on superior technology without having to buy cryptocurrencies.
Disclosure: I/we have no positions in any shares mentioned, and no plans to provoke any positions inside the subsequent seventy two hours. I wrote this text myself, and it expresses my very own opinions. i'm not receiving compensation for it (apart from from seeking Alpha). I have no business relationship with any business whose stock is mentioned listed here.
prior during this decade, when the hyperscalers and the lecturers that run with them have been building laptop discovering frameworks to transpose all kinds of facts from one structure to one other – speech to textual content, textual content to speech, picture to text, video to text, and the like – they were doing so now not just for scientific curiosity. They have been trying to remedy precise company issues and addressing the needs of purchasers the usage of their software.
on the same time, IBM turned into attempting to clear up a distinct problem, naming creating a query-answer gadget that would anthropomorphize the quest engine. This effort become referred to as mission Blue J interior of IBM (now not to be perplexed with the open supply BlueJ built-in building ambiance for Java), turned into wrapped up into a application stack referred to as DeepQA by means of IBM. It changed into this DeepQA stack, which changed into in response to the open source Hadoop unstructured records storage and analytics engine that got here out of Yahoo and an extra task referred to as Apache UIMA, which predates Hadoop by way of several years and which changed into designed through IBM database experts in the early 2000s to manner unstructured records like text, audio, and video. This Deep QA stack became embedded in the Watson QA equipment that became designed to play Jeopardy in opposition t people, which we referred to in detail here eight years in the past. The Apache UIMA stack became the key a part of the WatsonQA gadget that did herbal language processing that parsed out the speech in a Jeopardy answer, transformed it to textual content, and fed it into the statistical algorithms to create the Jeopardy query.
Watson received the contest in opposition t human Jeopardy champs Brad Rutter and Ken Jennings, and a company – which invoked IBM founder Thomas Watson and his admonition to “think” in addition to medical professional Watson, the sidekick of fictional supersleuth Sherlock Holmes – changed into born.
in preference to make Watson a product on the market, IBM provided it as a provider, and pumped the QA gadget full of information to tackle the healthcare, economic functions, power, promoting and media, and training industries. This was, possibly, a mistake, but at the time, within the wake of the Jeopardy championship, it felt like every thing turned into relocating to the cloud and that the SaaS mannequin become the correct method to go. IBM never in reality talked in superb element about how DeepQA turned into built, and it has in a similar fashion now not been particular about how this Watson stack has modified over time – eight years is a very long time within the computer getting to know area. It isn't clear if Watson is cloth to IBM’s revenues, but what is clear is that computer gaining knowledge of is strategic for its methods, application, and services organizations.
So it's why IBM is eventually bringing together all of its computing device learning tools and placing them below the Watson manufacturer and, very importantly, making the Watson stack obtainable for purchase so it can also be run on inner most datacenters and in other public clouds besides the one which IBM runs. To be genuine, the Watson capabilities as neatly as the PowerAI laptop learning working towards frameworks and adjunct equipment tuned up to run on clusters of IBM’s energy systems machines, are being brought together, and they'll be put into Kubernetes containers and allotted to run on the IBM Cloud private Kubernetes stack, which is purchasable on X86 programs as well as IBM’s personal power iron, in virtualized or bare metallic modes. It is that this encapsulation of this new and comprehensive Watson stack with IBM Cloud private stack that makes it transportable across deepest datacenters and other clouds.
by the way, as part of the mashup of those tools, the PowerAI stack that makes a speciality of deep studying, GPU-accelerated machine learning, and scaling and dispensed computing for AI, is being made a core part of the Watson Studio and Watson computer getting to know (Watson ML) utility tools. This built-in software suite offers enterprise statistics scientists an conclusion-to-end developer equipment. Watson Studio is an built-in development environment in response to Jupyter notebooks and R Studio. Watson ML is a collection of desktop and deep discovering libraries and mannequin and statistics management. Watson OpenScale is AI mannequin monitoring and bias and fairness detection. The software previously referred to as PowerAI and PowerAI business will continue to be developed by using the Cognitive techniques division. The Watson division, if you are not conventional with IBM’s organizational chart, is part of its Cognitive solutions community, which comprises databases, analytics equipment, transaction processing middleware, and a number of applications allotted either on premises or as a provider on the IBM Cloud.
it is doubtful how this Watson stack could exchange in the wake of IBM closing the crimson Hat acquisition, which may still occur before the end of the year. nonetheless it is competitively priced to anticipate that IBM will tune up all of this application to run on crimson Hat enterprise Linux and its personal KVM virtual machines and OpenShift implementation of Kubernetes and then push really challenging.
it's likely effective to review what PowerAI is all about and then exhibit how it is being melded into the Watson stack. earlier than the mixing and the identify adjustments (more on that in a second), here's what the PowerAI stack seemed like:
based on Bob Picciano, senior vp of Cognitive methods at IBM, there are more than 600 enterprise shoppers which have deployed PowerAI tools to run laptop researching frameworks on its power systems iron, and obviously GPU-accelerated programs like the power AC922 gadget this is on the coronary heart of the “Summit” supercomputer at very wellRidge national Laboratory and the sibling “Sierra” supercomputer at Lawrence Livermore country wide Laboratory are the main IBM machines americans are using to do AI work. here's a fine looking first rate start for a nascent business and a platform that is relatively new to the AI crowd, but most likely now not so different for commercial enterprise consumers which have used vigour iron of their database and application tiers for a long time.
The initial PowerAI code from two years ago began with models of the TensorFlow, Caffe, PyTorch, and Chainer computing device gaining knowledge of frameworks that massive Blue tuned up for its power processors. The big innovation with PowerAI is what's known as large model aid, which makes use of the coherency between Nvidia “Pascal” and “Volta” Tesla GPU accelerators and Power8 and Power9 processors in the IBM vigor programs servers – enabled with the aid of NVLink ports on the vigour processors and tweaks to the Linux kernel – to permit tons greater neural community practicing models to be loaded into the system. all the PowerAI code is open source and dispensed as code or binaries, and up to now simplest on vigor processors. (We suspect IBM will go agnostic on this eventually, on account that Watson equipment must run on the big public clouds, which with the exception now of the IBM Cloud, do not have vigor systems available. (Nimbix, a expert in HPC and AI and a smaller public cloud, does offer vigour iron and supports PowerAI, incidentally.)
below this, IBM has created a groundwork referred to as PowerAI enterprise, and here is now not open supply and it is barely attainable as part of a subscription. PowerAI commercial enterprise provides Message Passing Interface (MPI) extensions to the machine discovering frameworks – what IBM calls allotted Deep getting to know – as well as cluster virtualization and automated hyper-parameter optimization strategies, embedded in its Spectrum Conductor for Spark (sure, that Spark, the in-reminiscence processing framework) device. IBM has also added what it calls the Deep researching impact module, which includes tools for managing facts (comparable to ETL extraction and visualization of datasets) and managing neural community models, together with wizards that imply how to top of the line use statistics and models. On precise of this stack, IBM’s first industrial AI software that it's selling is known as PowerAI imaginative and prescient, which will also be used to label picture and video records for training models and instantly educate models (or augment latest models provided with the license).
So in any case of the changes, here is what the brand new Watson stack seems like:
As that you would be able to see, the Watson laptop studying stack supports much more laptop discovering frameworks, especially the SnapML framework that came out of IBM’s research lab in Zurich it truly is offering a significant efficiency knowledge on vigour iron in comparison to running frameworks like Google’s TensorFlow. here's obviously a extra comprehensive stack for laptop getting to know, including Watson Studio for establishing fashions, the vital Watson desktop gaining knowledge of stack for practising and deploying fashions in production inference, and now Watson OpenScale (it's mislabeled in the chart) to computer screen and help enhance the accuracy of models according to how they're working in the container as they infer things.
For the moment, there isn't any trade in PowerAI enterprise licenses and pricing all the way through the first quarter, but after that PowerAI business should be brought into the Watson stack to add the distributed GPU laptop studying practicing and inference capabilities atop vigor iron to that stack. So Watson, which begun out on Power7 machines taking part in Jeopardy, is coming lower back home to Power9 with production computer learning purposes within the business. We are not definite if IBM will present identical distributed computing device gaining knowledge of capabilities on non-vigour machines, however it looks likely it really is purchasers are looking to run the Watson stack on premises or in a public cloud, it is going to should. energy techniques will should stand by itself deserves if that involves move, and given the merits that Power9 chips have with reference to compute, I/O and reminiscence bandwidth, and coherent memory throughout CPUs and GPUs, that may additionally no longer be as a great deal of an have an effect on as we might feel. The X86 structure will ought to win by itself deserves, too.
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2. The Up & Comers: Health and Auto are big development areas for AI/ML due to the copious amount of data being created in those industries. For example, patient records house vital information related to disease identification and treatment. In Ontario alone, there are over 2.3 billion medical test results from 11.1 million Ontarians. On the other end of the spectrum, automobiles are constantly generating slews of data — distance traveled, average speed, destination — that can be used for optimizing traffic, city planning and accident prevention. For AI/ML to be effectively implemented within the enterprise, it needs to be integrated into core decision making processes within a company; just look at Facebook’s newsfeed algorithm and Uber’s surge pricing and route optimization system. AI/ML will replace a lot of the grunt work in the enterprise, freeing employee time for more value-add activities (e.g. Amy, x.ai’s robot assistant, who saves me from so many unnecessary emails when scheduling a meeting). Near-term opportunities in the enterprise are in data optimization, data search and messaging (think: commerce, payments and gaming), and you’ll likely see this quickly penetrate areas such as Enterprise Optimization, Law, Security and Sales.
A website's front-end performance should be the top priority for companies looking to improve conversion, according to GitHub software engineer Rachel Meyers. Unfortunately, performance is often overlooked or undervalued by management. "This is astounding because performance has a direct correlation to the interactions that you'll get, to the clicks that you'll get, to the page views that you'll get. If you're selling something it will track directly to conversion," Meyers explained. These business metrics are taken very seriously in most savvy organizations. The problem, according to Meyers, is that organizations are missing the causal relationship between performance and conversion.Sometimes businesses don't really value the performance of their website, which is astounding because it is the best way to improve conversion.
Rachel Meyers,software engineer, GitHub
At Fluent 2014, O'Reilly's annual conference, Meyers and Emily Nakashima, a GitHub UI engineer, proposed a new way to improve front end performance. Their talk, "Everything is a Polyfill: Automate Deleting Code for Front End Performance," shed light on the impact old code has on the speed and functionality of a website.
Meyers and Nakashima will offer advice on identifying code that no longer serves the front end, using automation tools to delete it and, most importantly, making a case for front-end performance in organizations that underestimate its importance.A new perspective on old code
Nakashima said that improving performance required a perspective change in regard to coding. "So much of it, it turns out, is actually temporary. It is revolving around specific constraints that might not last for a long time." For example, an Internet Explorer webpage in cascading style sheet (CSS) will have sections for IE6, IE7 and IE8. "That's extra CSS that oftentimes you're sending out in every single CSS file, not just the requests that are coming in from IE6 or IE8." In other words, the extra bandwidth spent on older browsers will slow down loading time on all the browsers. Simply pulling out the unnecessary pieces of code will speed up a website's overall performance.
Companies may be initially resistant to this idea because a lot of the deleted code will support old browsers that some customers still use. "I think a lot of people have an emotional reaction and they say, 'I always want to support all of my users, no matter how old their browser is.' But realistically, I think most companies end up looking at the usage of old browsers and drawing a line in the sand," Nakashima said.
She went on to say that automation tools can be applied to this problem in one of two ways. They can automatically delete code that reaches a predetermined expiration date, or they can send email reminders and notifications, raising company awareness of old code and its maintenance implications. A company should also consider conditional loading on the front end, which basically means selectively deleting polyfills for the customers that don't need them. "You can be smarter about how you load things and delete them for a subset of your users," Nakashima explained.Getting company buy-in
While the automation of deleting old code isn't necessarily a complex undertaking -- it does not require specialized skill sets or expensive tools -- development teams may still have one roadblock to overcome: management. "It takes a little bit of company buy-in to optimize your site. Sometimes businesses don't really value the performance of their website, which is astounding because it is the best way to improve conversion," Meyers said.
Luckily, this organizational challenge can be overcome by raising awareness. With the fast-paced onslaught of social media, mobile apps and websites that compete for user attention, the benchmark for a website's speed and functionality has been raised. As Nakashima put it, "Social sites have lifted people's expectations for how fast they'll see new content on a site. I think it prepares them for this rapid pace of interaction that raised the bar for how fast performance should be."
It is this gap between a user's performance expectation and a company's valuation of performance that Nakashima and Meyers hope to close. According to Meyers, their talk at the Fluent Conference is one step in that direction. "Fluent is a front-end conference, so we have this audience that comes from many different backgrounds. Most of them don't work performance into their everyday thinking, and so it's a perfect audience for us."
OpenWorld Oracle’s founder and CTO Larry Ellison took to the stage for his second OpenWorld keynote today, and spent the bulk of his presentation pointing out flaws in his chief cloud rival Amazon.
Amazon Web Services (AWS) has three database products running on its cloud (Redshift, Aurora and DynamoDB), and in terms of features they are all at least 20 years out of date, Ellison claimed. They are only able to run on AWS, are all proprietary, and they can’t be ported to other clouds, he complained.
“Build an app on Redshift and you will be running it forever on Amazon – you are locked in, baby,” he said. “So if Amazon raises its prices you better get out your checkbook.”
To demonstrate AWS’s failings on performance, Ellison and his team ran a speed test on an Oracle database hosted within the company’s own cloud compared to the same software running on an AWS X1 server. They found, surprise, surprise, that Amazon’s performance was 24 times slower for analytic queries and eight times slower for online transaction processing.
He then showed the results of pitting Redshift and Aurora against Oracle and the results weren’t good, apparently – Redshift conveniently ran 105 times slower than Oracle and Aurora was 35 times more sluggish. Oracle will put the benchmarks on its website [PDF] and Ellison challenged Amazon to try them itself and see if they can get any more speed out of their systems.
The problem with AWS, Ellison opined, was that it was running old first-generation infrastructure and its software is outdated. Amazon’s elastic block storage is limited to 48,000 IOPS per server and 800MB per second per server, making it an order of magnitude slower than Oracle’s cloud, he said.
As for the software, Ellison remarked that Redshift was still using table locking, something Oracle abandoned 30 years ago, and had no query optimization or table indexing – features that are 20 years old on his product. Aurora too was handicapped by its inability to scale to large sizes and its limited support for multithreaded remote replication.
Ellison did acknowledge Amazon used to have the edge on price, but now Oracle is hoping to take that away from them with the launch of its Exadata Express Cloud service. Prices start at $175 a month for a simplified version of the service, which has all the features of its more expensive counterpart but is designed for smaller databases. For a 20GB managed database, the new service would cost $175, compared to $245 for Aurora and $327 to run Oracle SE on Amazon RDS (Relational Database Service).
“We leapfrog Amazon with lower costs and higher performance, also in reliability and security,” he claimed. “We have more than ten times the IOPS capacity, but you have to pay less.”
Funny how things change. Six years ago, his company was practically falling over itself to run its software on AWS. Let us know, in the comments below, what you think of Larry's trash talk. ®
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