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000-633 | Object Oriented Analysis and Design - Part 1

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Exam Number 000-633
Exam Name Object Oriented Analysis and Design - Part 1
Questions 105 Q & A
Recent Update July 11, 2018
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000-633 exam Dumps Source : Object Oriented Analysis and Design - Part 1

Test Code : 000-633
Test Name : Object Oriented Analysis and Design - Part 1
Vendor Name : IBM
Q&A : 105 Real Questions

IBM Object Oriented Analysis

artificial Intelligence Has a Bias difficulty, and or not it's Our Fault | killexams.com Real Questions and Pass4sure dumps

AI Has a Bias Problem, and It's Our Fault

In 2016, researchers from Boston school and Microsoft have been working on artificial intelligence algorithms after they discovered racist and sexist tendencies in the know-how underlying some of the most normal and significant services we use daily. The revelation went towards the widespread knowledge that artificial intelligence does not undergo from the gender, racial, and cultural prejudices that we people do.

The researchers made this discovery whereas discovering observe-embedding algorithms, a kind of AI that finds correlations and associations amongst different phrases with the aid of examining big our bodies of text. as an instance, a trained be aware-embedding algorithm can needless to say phrases for flowers are carefully related to gratifying emotions. On a extra useful degree, word embedding is aware that the term "computing device programming" is intently related to "C++," "JavaScript" and "object-oriented evaluation and design." When built-in in a resume-scanning software, this performance lets employers discover qualified candidates with less effort. In search engines like google, it could supply more desirable effects by means of mentioning content that's semantically concerning the quest time period.

The BU and Microsoft researchers discovered that the note-embedding algorithms had frustrating biases, even though—similar to associating "laptop programmer" with male pronouns and "homemaker" with female ones. Their findings, which they posted in a research paper aptly titled "Man is to laptop Programmer as woman is to Homemaker?" become one in every of several experiences to debunk the fable of AI neutrality and to shed mild on algorithmic bias, a phenomenon it's reaching essential dimensions as algorithms turn into more and more concerned in our popular choices.

The Origins of Algorithmic Bias

computing device discovering and deep-getting to know algorithms underlie most contemporary AI-powered application. In contrast to average software, which works based on predefined and verifiable guidelines, deep discovering creates its personal rules and learns by means of example.

for instance, to create a picture-recognition utility according to deep discovering, programmers "train" the algorithm through feeding it labeled facts: in this case, pictures tagged with the identify of the thing they comprise. once the algorithm ingests satisfactory examples, it could glean general patterns among in a similar way labeled facts and use that counsel to categorise unlabeled samples.

This mechanism makes it possible for deep getting to know to operate many initiatives that have been essentially unimaginable with rule-primarily based software. but it surely also potential deep-getting to know application can inherit covert or overt biases.

"AI algorithms aren't inherently biased," says Professor Venkatesh Saligrama, who teaches at Boston school's branch of Electrical and laptop Engineering and worked on the be aware-embedding algorithms. "they've deterministic performance and will prefer up any tendencies that already exist in the records they instruct on."

The notice-embedding algorithms verified by means of the Boston institution researchers were informed on lots of of lots of articles from Google news, Wikipedia, and other online sources wherein social biases are deeply embedded. as an instance, on account of the bro lifestyle dominating the tech business, male names come up extra frequently with tech-connected jobs—and that leads algorithms to affiliate men with jobs corresponding to programming and application engineering.

ai bias face

"Algorithms would not have the power of the human intellect in distinguishing right from incorrect," adds Tolga Bolukbasi, a last-12 months PhD scholar at BU. humans can judge the morality of our actions, even once we make a decision to act towards ethical norms. but for algorithms, statistics is the top-rated settling on ingredient.

Saligrama and Bolukbasi weren't the first to lift the alarm about this bias. Researchers at IBM, Microsoft, and the institution of Toronto underlined the need to stay away from algorithmic discrimination in a paper published in 2011. lower back then, algorithmic bias changed into an esoteric situation, and deep researching nevertheless hadn't discovered its way into the mainstream. today, notwithstanding, algorithmic bias already leaves a mark on lots of the things we do, similar to reading news, finding chums, shopping on-line, and staring at videos on Netflix and YouTube.

The have an effect on of Algorithmic Bias

In 2015, Google had to say sorry after the algorithms powering its photos app tagged two black americans as gorillas—most likely as a result of its training dataset did not have satisfactory photos of black individuals. In 2016, of the forty four winners of a elegance contest judged by means of AI, basically all were white, a couple of had been Asian, and just one had dark epidermis. again, the intent became that the algorithm become primarily trained with photographs of white people.

extra lately, a check of IBM and Microsoft's face-evaluation services found the companies' algorithms have been virtually flawless at detecting the gender of men with easy skin but often erred when offered with images of girls with darkish dermis.

whereas these incidents probably led to negligible damage, the equal can't be spoke of of AI algorithms in more vital domains, equivalent to health care, law enforcement, and recruitment. In 2016, an investigation through ProPublica found that COMPAS—AI-pushed software that assesses the risk of recidivism in offenders—became biased against people of color. the discovery became mainly regarding as a result of judges in some states use COMPAS to verify who walks free and who stays in prison.

In an additional case, a look at of Google's promoting platform, which is powered by means of deep-getting to know algorithms, discovered that guys had been proven adverts for high-paying jobs greater commonly than ladies were. A separate look at discovered the same situation with LinkedIn's job ads. Yet a further confirmed that biased hiring algorithms have been 50 percent more more likely to ship an interview invitation to a person whose identify turned into European-American than to somebody with an African-American name.

Areas reminiscent of mortgage approval, credit standing, and scholarship face identical threats.

ai bias head

Algorithmic bias is further being concerned on account of the way it might extend social biases. beneath the illusion that AI is bloodless, mathematical calculation devoid of prejudice or bias, humans may additionally are inclined to trust algorithmic judgment without questioning it.

In an interview with Wired UK, Edinburgh Napier institution criminology lecturer Andrew Wooff observed that the "time-compelled, useful resource intensive" world of policing might trigger legislations enforcement officers to rely too a lot on algorithmic choices. "i will be able to imagine a condition the place a police officer may also rely more on the [AI-driven] equipment than their own resolution-making procedures," he mentioned. "Partly that can be so that you should justify a call when anything goes wrong."

relying on biased algorithms creates a comments loop: We make choices that create extra biased information that algorithms will then analyze and teach on in the future.

This type of thing is already going on on social media networks comparable to fb and Twitter. Algorithms running the information feeds create "filter bubbles," which reveal content that conforms to clients' preferences and biases. This could make them much less tolerant toward opposing views and may additionally extra polarize society by using using a wedge in the course of the political and social divide.

"Algorithmic bias could potentially have an effect on any community," says Jenn Wortman Vaughan, senior researcher at Microsoft. "companies that are underrepresented in the facts may well be specifically in danger."

In domains which are already common for bias, such as the tech business's endemic discrimination against women, AI algorithms may intensify these biases and influence in further marginalization of organizations that aren't well represented.

fitness is a different essential area, Wortman features out. "It might trigger critical complications if a computer learning algorithm getting used for scientific diagnosis is proficient on statistics from one inhabitants and, because of this, fails to operate well on others," she says.

Bias can even be damaging in additional delicate methods. "remaining 12 months i used to be planning to take my daughter for a haircut and searched online for photos of 'toddler haircuts' for notion," Wortman says. but the photos again had been practically all of white babies, primarily with straight hair, and greater quite, primarily boys, she seen.

consultants call this phenomenon "representational harm": when expertise reinforces stereotypes or diminishes selected organizations. "it be tough to quantify or measure the accurate impact of this form of bias, but that does not imply or not it's now not critical," Wortman says.

casting off Bias From AI Algorithms

The increasingly essential implications of AI bias have drawn the consideration of a number of corporations and govt bodies, and some wonderful steps are being taken to tackle the moral and social concerns surrounding the use of AI in distinct fields.

Microsoft, whose items rely closely on AI algorithms, launched a research undertaking three years ago referred to as fairness, Accountability, Transparency, and Ethics in AI (fate) geared toward enabling users to enjoy the greater insights and effectivity of AI-powered features devoid of discrimination and bias.

In some circumstances, just like the AI-adjudicated beauty contest, discovering and fixing the supply of an AI algorithm's biased habits might possibly be as handy as checking and altering out the pictures within the practising dataset. but in different instances, such because the observe-embedding algorithms the Boston university researchers examined, bias is engrained in the training statistics in additional subtle methods.

The BU team, which became joined by way of Microsoft researcher Adam Kalai, developed a technique to classify note embeddings in line with their gender categorizations and identify analogies that have been doubtlessly biased. but they did not make the remaining resolution and would run each of the suspect associations through 10 individuals on Mechanical Turk, Amazon's online marketplace for facts-linked initiatives, who would make a decision whether the association may still be eliminated or no longer.

ai bias city

"We did not wish to insert our personal biases into the manner," says Saligrama, the BU professor and researcher. "We simply provided the equipment to find tricky associations. people made the remaining choice."

In a greater recent paper, Kalai and different researchers proposed the use of separate algorithms to classify diverse companies of americans in its place of using the same measures for each person. This system can prove positive in domains where latest information is already biased in favor of a particular group. for example, the algorithms that could evaluate female candidates for a programming job would use standards that are finest proper for that group as a substitute of using the broader set of records that's deeply influenced through existing biases.

Microsoft's Wortman sees inclusiveness in the AI business as a crucial step to battle bias in algorithms. "If we desire our AI techniques to be beneficial to everybody and never simply certain demographics, then companies deserve to be hiring different groups to work on AI," she says.

In 2006, Wortman helped found women in machine researching (WiML), which holds a each year workshop at which women learning and working within the AI trade can meet, network, trade ideas, and attend panel discussions with senior ladies in business and academia. a similar effort is the new Black in AI Workshop, founded by using Timnit Gebru, one other Microsoft researcher, which aims to build extra distinct talent in AI.

Boston tuition's Bolukbasi also proposes altering the manner AI algorithms clear up problems. "Algorithms will select a rule set that maximizes their aim. There could be many how you can attain the equal set of conclusions for given input output pairs," he says. "Take the illustration of varied-option checks for people. One might also reach the appropriate reply with a incorrect considering manner, however then again get the identical score. an excellent examine should be designed to lower this impact, simplest permitting the individuals that in reality know the area to get suitable rankings. Making algorithms aware of social constraints can also be viewed as an analog to this illustration (besides the fact that children no longer an accurate one), where studying a incorrect rule set is penalized within the aim. this is an ongoing and challenging research subject."

AI's Opacity Complicates fairness

ai bias hammer

an extra challenge standing within the manner of making AI algorithms fairer is the "black box" phenomenon. in lots of situations, groups jealously guard their algorithms: as an instance, Northpointe Inc., the company of COMPAS, the crime-predicting utility, has refused to divulge its proprietary algorithm. The best individuals privy to COMPAS' internal workings are its programmers, now not the judges the use of it to circulate judgment.

aside from corporate secrecy, AI algorithms now and again develop into so convoluted that the reasons and mechanisms in the back of their choices elude even their creators. in the UK, Durham police use AI gadget HART to verify even if suspects have a low, moderate, or high chance of committing additional crimes within a two-year period. however a 2017 tutorial overview of HART observed that "opacity seems complicated to steer clear of." here is partly as a result of the sheer quantity and range of information the equipment uses, which makes it tricky to analyze the explanations in the back of its choices. "These details may be made freely attainable to the public, however would require a huge period of time and energy to fully bear in mind," the paper says.

a number of companies and companies are main efforts to carry transparency to AI, together with Google, which has launched GlassBox, an initiative to make the behavior of computing device-learning algorithms greater understandable devoid of sacrificing output great. The protection advanced research tasks agency (DARPA), which oversees using AI within the armed forces, is additionally funding an effort to enable AI algorithms to clarify their selections.

In different situations, human judgment can be key in coping with bias. To keep away from existing racial and social human biases from creeping into HART's algorithms, the Durham Constabulary supplied participants of its team of workers with recognition sessions round unconscious bias. The police drive has also taken steps to eliminate records facets similar to racial characteristics, which could create the grounds for biased decisions.

Human responsibility

From a different point of view, AI algorithms can deliver a chance to reflect on our own biases and prejudices. "the world is biased, the old data is biased, hence it is not mind-blowing that we receive biased consequences," Sandra Wachter, a researcher in records ethics and algorithms on the institution of Oxford, instructed The Guardian.

Wachter is a part of a research group from the Alan Turing Institute in London and the institution of Oxford, which published a paper calling for laws and associations to investigate viable discrimination via AI algorithms.

additionally chatting with The Guardian, Joanna Bryson, a pc scientist on the institution of bathtub and the coauthor of a research paper on algorithmic bias, observed, "lots of people are announcing [algorithmic bias] is showing that AI is prejudiced. No. this is showing we're prejudiced and that AI is learning it."

In 2016, Microsoft launched Tay, a Twitter bot that was alleged to be trained from humans and interact in smart conversations. but within 24 hours of Tay's launch, Microsoft had to shut it down after it began spewing racist feedback, which it had picked up from its conversations with Twitter clients. in all probability this is a reminder that it is past time that we humans renowned our personal function within the apparition and propagation of the algorithmic bias phenomenon and take collective steps to undo its effects.

"here's a really advanced assignment, but it is a responsibility that we as society may still no longer shy away from," Wachter says.

Digital twin AIs designed to be trained at the facet | killexams.com Real Questions and Pass4sure dumps

Chris Middleton, Vinelake


With Pure Storage and NVIDIA recently launching their artificial intelligence (AI) supercomputer, it is effortless to believe commercial enterprise-grade AI is entirely about throwing big number-crunching capacity at huge information units and seeing what patterns emerge. however while these applied sciences notionally are aimed toward all types of company, the can charge of optimized AI hardware that can also be slotted into a knowledge center could be too high for many groups.

at the different end of the size are applied sciences corresponding to IBM's Watson and Watson Assistant—which may also be deployed as cloud services—and of course numerous suite-based mostly AI tools at the moment provided via many businesses. besides the fact that children, for a lot of cyber web of issues (IoT) and linked-equipment deployments, neither data core nor cloud options are functional, which is why many AI programs are relocating somewhere else, speedy.

For time-vital processing—akin to when an self reliant car needs to evade a collision—the aspect ambiance and the dispensed core are the place the actual number crunching needs to take region. this is why companies equivalent to Microsoft and Dell have announced new IoT recommendations focused principally on the facet and/or the dispensed core. The capability to add AI on the part is an increasingly essential point in the IoT, avoiding the deserve to switch giant quantities of records to supercomputers or the cloud and lower back once again to IoT networks.

Startup SWIM.AI aims to "turn any edge equipment into an information scientist," devoid of the need for massive information sets and the enterprise-grade quantity crunching that goes with them. 

Twin options

The business's AI side product, EDX, is designed to autonomously build digital twins without delay from streaming facts in the aspect environment. The system is constructed for the emerging IoT world wherein true-world gadgets aren't just interconnected, but additionally present digital representations of themselves, which will also be immediately made from, and constantly up-to-date with the aid of, data from their true-world siblings.

Digital twins are digital representations of a true-world object, entity, or gadget, and are created both merely in statistics or as three-D representations of their physical counterparts. as an example, every component of the largest machine in background, the giant Hadron Collider, is stored as a digital twin in CERN's business asset management (EAM) equipment. This permits scientists to not simplest understand where every little thing is and what it appears like, however additionally how smartly add-ons are performing and after they need upgrade, restore, or alternative.

for many agencies, however, that type of large, bespoke software is never an alternative. They want whatever less demanding, more straightforward to deploy, and more affordable. 

Predictive twins

SWIM's EDX gadget is designed to allow digital twins to research, learn, and predict their future states from their personal precise-world records. in this means, programs can use their own conduct to instruct accurate behavioral fashions via deep neural networks. The crucial change to different AI options is this potential is offered as a service in true-time, with out centralized, batch-oriented large-statistics evaluation. SWIM EDX functions consist of wise cities, industrial automation, utilities, and guidance technology (IT) infrastructure optimization. 

Twin administration

Gartner views digital twins as probably the most exact strategic enterprise developments in 2018. although, a key problem is how enterprises can implement the know-how, given their investments in legacy property.

SWIM believes restrained ability sets in streaming analytics, coupled with an frequently negative figuring out of the assets that generate facts inside advanced IoT systems, make deploying digital twins too complicated for some. meanwhile, the prohibitive can charge of some digital twin infrastructures places other agencies off.

"Digital twins need to be created in accordance with specified understanding of how the property they represent operate, and they need to be paired with their precise-world siblings to be beneficial to stakeholders on the front line," observed SWIM in an announcement. "Who will operate and manipulate digital twins? the place will the supporting infrastructure run? How can digital twins be married with business resource planning (ERP) and other applications, and the way can the expertise be made constructive for agile enterprise decisions?"

The company claims SWIM EDX addresses these challenges by way of enabling any organization with a lot of data to create digital twins that be taught from the actual world always, and to accomplish that readily, cheaply, and automatically.

Chris Middleton is the editor of internet of company (IoB), a CFE Media content companion. this text at the start appeared right here. Edited by way of Chris Vavra, creation editor, CFE Media, cvavra@cfemedia.com.

greater INSIGHTS

keywords: part computing, AI

The capability to add artificial intelligence (AI) at the side is an increasingly important element for the cyber web of things (IoT) as companies seem to increase records processing and effectivity. Digital twins are digital representations of a true-world object, entity, or device, and might be improved with AI and the IoT. go browsing examine this article at www.controleng.com for more information about aspect computing and AI purposes. believe this What different improvements will also be made to digital twin expertise to support AI and the IoT?

IBM : declares New Pricing model to exchange Economics of Cloud Storage | killexams.com Real Questions and Pass4sure dumps

LAS VEGAS, March 20, 2017 /PRNewswire/ -- IBM InterConnect -- IBM (NYSE: IBM) nowadays unveiled new additions to the IBM Cloud Object Storage family. The company is providing shoppers new decisions for archival facts and a new pricing mannequin to extra simply follow intelligence to unpredictable records patterns the usage of analytics and cognitive tools. IBM also announced an multiplied partnership with NetApp that gives NetApp valued clientele with extra tips on how to take skills of IBM Cloud.

groups typically must manage throughout three types of information workloads: "hot" for facts that's often accessed and used; "cool" for facts this is occasionally accessed and used; and "cold" for archival records. bloodless storage is commonly defined as cheaper, however slower; as an example, if a company uses bloodless storage, it customarily has to wait prolonged periods of time to entry facts, limiting the means to impulsively use that records to derive insights. due to this fact, there is an inclination to keep records in more costly hot storage.

IBM's new cloud storage carrier, IBM Cloud Object Storage Flex (Flex), uses a "pay as you use" mannequin of storage tiers potentially lowering the price by using 53 % compared to AWS S3 IA(1) and seventy five percent compared to Azure GRS Cool Tier.(2) Flex is a brand new cloud storage carrier offering simplified pricing for shoppers whose information usage patterns are intricate to foretell. Flex allows customers to advantage from the charge reductions of bloodless storage for rarely accessed information, while holding excessive accessibility to all statistics.

additionally, the IBM Cloud Object Storage cold Vault (cold Vault) carrier gives valued clientele access to bloodless storage facts on the IBM Cloud and is designed to guide the class for cold information healing instances among main competitors. cold Vault joins the latest general and Vault tiers to comprehensive a variety of cloud storage tiers that can be found with multiplied advantage and techniques via Bluemix and through the IBM Bluemix Garages.

"customers want sooner entry to extra of their information to develop into cognitive corporations with a deep knowing of markets and consumer needs," noted John Morris, normal manager, IBM Cloud Object Storage. "IBM's new cloud storage functions and creative pricing model provides clients an efficient and less expensive way to behave on insights from unpredictable data patterns."

IBM and NetApp expand partnership with new cloud object storage solutions

NetApp and IBM are increasing an extended-standing partnership with the launch of latest capabilities that enable NetApp shoppers to take expertise of IBM Cloud.

NetApp's AltaVault(3) cloud backup options will now instantly be capable of send backups to IBM Cloud Object Storage on IBM Cloud. Incorporating native assist for IBM Cloud Object Storage into AltaVault options makes it possible for NetApp users to install backup facts to the IBM Cloud. It helps reduce needless information core growth and makes it possible for AltaVault users to take talents of IBM Cloud Object Storage's flexibility, safety and regional fault tolerance.

"Our multiplied partnership with IBM offers NetApp consumers with new how to take talents of elastic cloud economics the use of IBM Cloud," referred to Henri Richard, govt vp, worldwide box and consumer Operations, NetApp. "These new solutions extra the development of our facts textile approach and support our management in assisting shoppers seamlessly manipulate their records within the hybrid cloud period."

As a part of the contract, NetApp will present backup and archival solutions that integrate with IBM Cloud Object Storage by way of income channels.

This announcement enhances IBM's resiliency functions together with these provided through IBM global enterprise functions.

InterConnect is IBM's cloud and cognitive convention where more than 20,000 builders, clients and companions are being brought to the latest developments in cloud computing via 2,000 periods, labs and certifications. IBM is positioning each commercial enterprise and startup valued clientele for fulfillment with an entire portfolio of cloud capabilities and marquee partnerships, helping a wide range of functions including: large records, analytics, blockchain and cognitive computing.

For extra counsel, consult with: https://www.ibm.com/cloud-computing/. For the IBM InterConnect Press equipment, talk over with: ibm.biz/IBMInterConnect2017. engage within the dialog via @IBMCloud and #ibminterconnect.


-- IBM Cloud Object Storage cold Vault is attainable in IBM Bluemix. -- IBM Cloud Object Storage Flex can be available by the use of IBM Bluemix in Q2 2017. -- IBM Bluemix garage locations international, starting in April, might be equipped to work with valued clientele on digital transformation journeys inclusive of IBM Cloud Object Storage.

About IBM

For greater information on IBM Cloud Object Storage, consult with www.ibm.com/cloud-computing/items/storage/object-storage/.


(1) IBM Cloud Object Storage Flex go-location vs. S3 "scorching": commonplace bucket in AWS US East ("primary") with cross area Replication to S3 rare entry bucket in US West Oregon ("replica") and vs. S3 "Cool": S3 rare entry bucket in AWS US East with cross place Replication to S3 infrequent access bucket in US West Oregon. All S3 access assumed via "simple" bucket. Pricing in keeping with Amazon US checklist fees as of 3/6/2017 and announced IBM Cloud Object Storage "Flex" pricing with deliberate availability for April 2017. expense contains storage potential, API operations, and move-area records replication charges (S3 simplest). expense comparisons exclude outbound internet facts transfer charges. usage based pricing evaluation in line with certain workload assumptions -exact consumer expenses will vary depending on workload, storage skill, object sizes, statistics entry patterns, storage tier, redundancy options, and other elements. Pricing for this comparison based on here workload assumptions:

-- blended footprint of 5% "small" and ninety five% "massive" objects (by capacity), common object sizes: Small = 3MB, massive = 275MB -- monthly entry patterns: "hot": eighty% study, 50% written, 5% listed. "Cool": 10% read, 50% written, 5% listed. All objects assumed retained at the least 30 days.

IBM Cloud Object Storage: https://www.ibm.com/cloud-computing/products/storage/object-storage/public-carrier/

Amazon AWS S3: http://docs.aws.amazon.com/AmazonS3/newest/dev/crr.html, https://aws.amazon.com/s3/faqs/#crr, https://aws.amazon.com/s3/pricing


IBM Cloud Object Storage Flex pass-location vs. Azure hot GRS in East US 2 (fundamental) and vs. Azure Cool GRS in East US 2 (simple). Pricing in accordance with Azure US list costs as of three/6/2017 and introduced IBM Cloud Object Storage "Flex" pricing with deliberate availability for April 2017. fee contains storage capacity, API operations, and statistics replication prices (Azure only). cost comparisons exclude outbound web records transfer expenses. usage based mostly pricing assessment in response to certain workload assumptions -actual customer charges will differ counting on workload, storage capability, object sizes, statistics entry patterns, storage tier, redundancy alternate options, and different elements. Pricing for this comparison in line with here workload assumptions:

-- mixed footprint of 5% "small" and ninety five% "tremendous" objects (by using means), typical object sizes: Small = 3MB, colossal = 275MB -- monthly access patterns: "scorching": eighty% read, 50% written, 5% listed. "Cool": 10% read, 50% written, 5% listed.

IBM Cloud Object Storage: https://www.ibm.com/cloud-computing/items/storage/object-storage/public-carrier/

Azure GRS: https://azure.microsoft.com/en-us/pricing/particulars/storage/blobs, https://doctors.microsoft.com/en-us/azure/storage/storage-blob-storage-tiers

(three )NETAPP, the NETAPP emblem and AltaVault are logos of NetApp, Inc.

Media Contact:Greg VitarelliIBM Media members of the family(617) 455-9627[email protected]

To view the original edition on PR Newswire, discuss with:http://www.prnewswire.com/news-releases/ibm-proclaims-new-pricing-model-to-change-economics-of-cloud-storage-300425941.html

supply IBM

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