000-M602 exam Dumps Source : IBM Banking Industry Solutions Sales(R) Mastery Test v1
Test Code : 000-M602
Test appellation : IBM Banking Industry Solutions Sales(R) Mastery Test v1
Vendor appellation : IBM
: 30 true Questions
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Media insurance about IBM (NYSE:IBM) has been trending neutral this week, in accordance with InfoTrie Sentiment. The research group ranks the sentiment of word insurance via examining more than six thousand word and blog sources. The firm ranks coverage of corporations on a scale of -5 to five, with rankings nearest to five being essentially the most favorable. IBM earned a media sentiment score of 0.50 on their scale. InfoTrie furthermore gave press coverage in regards to the expertise enterprise an information buzz score of 1 out of 10, indicating that concurrent word coverage is extremely unlikely to believe an repercussion on the company’s participate rate within the near future.
listed below are one of the vital media stories that may furthermore believe impacted IBM’s evaluation:
NYSE:IBM opened at $133.19 on Friday. The company has a debt-to-equity ratio of 2.10, a brief ratio of 1.24 and a present ratio of 1.29. IBM has a twelve months low of $one hundred and five.94 and a one year lofty of $162.11. The company has a market cap of $123.89 billion, a P/E ratio of 9.64, a price-to-income-boom ratio of 2.forty and a beta of 1.25.
IBM (NYSE:IBM) closing posted its earnings outcomes on Tuesday, January 22nd. The know-how industry suggested $four.87 EPS for the quarter, topping the consensus estimate of $4.82 via $0.05. IBM had a web margin of 10.97% and a recrudesce on fairness of sixty eight.sixty one%. The industry had profits of $21.76 billion for the quarter, in comparison to the consensus estimate of $21.seventy nine billion. any over the equal length final yr, the company earned $5.14 EPS. The company’s profits turned into down three.5% compared to the identical quarter closing 12 months. On general, analysts predict that IBM will submit 13.92 profits per participate for the latest fiscal year.
The enterprise furthermore recently introduced a quarterly dividend, which will live paid on Saturday, March ninth. investors of listing on Friday, February eighth could live given a dividend of $1.57 per share. The ex-dividend date is Thursday, February 7th. This represents a $6.28 annualized dividend and a bow of four.seventy two%. IBM’s dividend payout ratio is perquisite now 45.47%.
IBM declared that its Board of directors has initiated a participate buyback application on Tuesday, October 30th that makes it feasible for the industry to repurchase $four.00 billion in brilliant shares. This repurchase authorization allows for the know-how enterprise to reacquire up to three.5% of its stock through open market purchases. inventory repurchase classes are often an indication that the enterprise’s board believes its shares are undervalued.
a yoke of equities research analysts believe lately weighed in on the inventory. Zacks funding analysis downgraded shares of IBM from a “hold” ranking to a “sell” ranking in a research notice on Thursday, January 24th. Wedbush restated a “hold” rating and issued a $131.00 rate target on shares of IBM in a analysis observe on Wednesday, January twenty third. BMO Capital Markets raised their fee goal on shares of IBM to $147.00 and gave the company a “market operate” ranking in a research note on Wednesday, January twenty third. They cited that the stream became a valuation call. Royal pecuniary institution of Canada restated an “outperform” score and issued a $one hundred sixty.00 charge goal (up from $a hundred and fifty five.00) on shares of IBM in a research word on Wednesday, January 23rd. eventually, Citigroup raised their cost goal on shares of IBM from $a hundred twenty five.00 to $130.00 and gave the company a “buy” ranking in a analysis note on Wednesday, January twenty third. 4 analysts believe rated the inventory with a promote score, ten believe assigned a dangle ranking and eight believe assigned a purchase ranking to the enterprise’s inventory. IBM has a consensus score of “hold” and a consensus target cost of $154.fifty six.
In other information, insider Diane J. Gherson offered 5,754 shares of the enterprise’s stock in a transaction that befell on Wednesday, February 6th. The shares were offered at a regular fee of $one hundred thirty five.67, for a total cost of $780,645.18. Following the transaction, the insider now owns 23,117 shares in the enterprise, valued at about $3,136,283.39. The sale become disclosed in a filing with the Securities & change fee, which may furthermore live accessed during the SEC site. 0.17% of the inventory is presently owned by means of insiders.
COPYRIGHT VIOLATION observe: “IBM (IBM) Getting impartial Press insurance, InfoTrie reviews” turned into initially published by means of Dispatch Tribunal and is the only property of of Dispatch Tribunal. if you are studying this fable on another website, it become illegally stolen and reposted in violation of united states and foreign copyright legislations. The proper edition of this fable can live accessed at https://www.dispatchtribunal.com/2019/02/08/ibm-ibm-getting-impartial-press-coverage-infotrie-experiences.html.
IBM enterprise Profile
international company Machines company operates as an built-in technology and services industry international. Its Cognitive options facet presents Watson, a computing platform that interacts in language, techniques huge records, and learns from interactions with individuals and computers. This segment additionally presents facts and analytics options, together with analytics and records administration systems, cloud data functions, commercial enterprise companionable software, skill administration solutions, and tailor-made industry solutions; and transaction processing application that runs mission-vital systems in banking, airlines, and retail industries.
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January 14, 2008 08:fifty nine ET
HANOI, VIETNAM--(Marketwire - January 14, 2008) - nowadays IBM (NYSE: IBM) introduced that it'll establish a Banking core of Excellence in Vietnam within the first quarter of 2008. The core will permit valued clientele in economic features to build and set up subsequent-technology core banking systems and fresh functions based on open technologies. Banks might live capable of utilize supplies on the core to satisfy growing purchaser demand for brand spanking fresh banking products.
The banking heart will live institute in the IBM Vietnam headquarters in Hanoi and staffed with IBM banking and technical experts. The heart will assist shoppers and companions with enterprise and know-how design, supplier and equipment preference, modeling, proof of idea, benchmark trying out and system migration. The middle will assist to provide lofty efficiency computing solutions that guarantee fine and safety during the finished lifestyles cycle of banking operations.
moreover, in 2008 IBM will live making immense investments to enhance a framework in response to services oriented structure (SOA) to support banks modernize their core banking techniques.
A bank's core device provides the groundwork for its industry operations. for instance, it tactics transactions to update client tag downs and credit card bills and provides bank-to-bank features akin to wire switch and treasury administration. IBM's fresh banking middle will support banks in Vietnam enhance their core systems, increasing effectivity, flexibility and the capability to scale to aid the Vietnamese banking trade's quick increase.
at the coronary heart of the fresh heart is the IBM system z platform, which provides the area-class availability, protection and resiliency banks require in trendy on-demand fiscal capabilities ambiance. valued clientele might live capable of utilize IBM open infrastructure applied sciences together with net capabilities and virtualization; and software equipment similar to DB2, WebSphere and Tivoli. together, these technologies will support a provider oriented structure to design disparate records attainable throughout multiple traces of enterprise inside an business. as a result, banks can live capable of greater without problems combine customer information throughout account varieties, enhancing customer delight.
"As a number one economic capabilities company in a unexpectedly expanding purchaser market, we're delighted to believe IBM convey their event with banks everywhere to Vietnam," mentioned Mr. Dao Minh Tuan, CIO of Vietcombank. "we're keen to utilize such skills and technologies to present world-class monetary items for their purchasers."
"Vietnam's transforming into economic system represents an outstanding haphazard for IBM and the country's banking trade, because it looks to convey fresh products and features to fulfill the transforming into fiscal needs of consumers," referred to Steve Mills, Senior vice president and neighborhood govt, IBM utility.
"IBM's Banking core of Excellence may live a globally-built-in useful resource for banks in Vietnam to construct next-generation core banking techniques," brought Ian Hurst, IBM's accustomed supervisor of Banking and monetary Markets Industries.
In concert with the turning out to live pecuniary system, a myriad of latest banking functions are getting regularly occurring in Vietnam. in line with the status pecuniary institution of Vietnam, in 2006 the number of ATMs grew 21%, whereas the number of pecuniary institution cards grew 30%. on account that handiest a small percentage of the inhabitants is the usage of these functions today, Vietnam's increasing banking sector represents a major break for increase.
"We welcome IBM's Banking middle of Excellence," talked about Mr. Ta Quang Tien, Director of suggestions know-how department on the status bank of Vietnam. "The core will support banks in Vietnam reside aggressive with the latest applied sciences, in an application to serve the requisite generated by way of anticipated growth."
the fresh Banking middle of Excellence in Vietnam will live fragment of an present world network of IBM banking centers that had been established in 2007. They consist of centers at IBM's Silicon Valley Lab in California, operated by means of the IBM inordinate performance On demand solutions (HiPODS) Lab; the IBM Poughkeepsie Lab in ny; and the IBM software options Lab in Seoul, Korea. These facilities support shoppers tackle complicated company challenges, similar to international competition, shifting markets, enterprise model transformation, altering rules, rising working fees, and the demand for an expanding array of economic items. together, the facilities characterize a multi-million dollar investment with the aid of IBM.
For more assistance in regards to the Banking heart of Excellence, delight consult with http://www-01.ibm.com/methods/z/feature022607/
For extra tips about IBM's inordinate efficiency Computing On demand solutions Lab, delight consult with http://www-128.ibm.com/developerworks/websphere/zones/hipods/
For greater assistance about IBM device z, delight talk over with http://www-03.ibm.com/programs/z/
Feb 01, 2019 (Euclid Infotech Ltd via COMTEX) -- IBM and the bank of the Philippine Islands (BPI), a universal bank headquartered in Manila, believe signed a $260M, multi-yr capabilities settlement. IBM will provide IT infrastructure capabilities to guide an agile IT and hybrid cloud, in addition to digital structure capabilities to hasten up BPI's digital transformation.
BPI is the third-largest bank in the Philippines in terms of assets, with greater than 9 million shoppers. The bank normally improves the digital banking event for valued clientele by means of investing in expertise, including this renewed IT features agreement with IBM, throughout on-line, mobile apps, ATMs, CAMs (money accept Machines) and branches.
BPI goals to preserve an agile and compliant IT environment to preserve leadership within the Philippine banking trade. To obtain this, BPI will leverage IT infrastructure and hybrid cloud management functions from IBM that promises seamless technology in spite of no matter if it resides inside BPI's existing infrastructure or its private or public cloud environments. In doing so, IBM will host the bank's digital solutions of today and in the coming years.
As fragment of the Digital adventure answer, IBM and BPI believe furthermore designed an IT environment that helps an open API pecuniary system strategy that makes the lots of the bank's facts to create compelling client functions and services. The contract will support to obtain one of the vital bank's digital transformation dreams which encompass the interminable enhancements for pecuniary institution branches with fresh know-how as well as other monetary capabilities apps for valued clientele.
The features offered via IBM will believe "born in the cloud experiences" at its core to assist BPI's Hybrid Cloud approach, together with Infrastructure as Code, and an open API consumption. IBM protection features will underpin the success of the IT services mode designed by IBM for BPI.
"The unexpectedly altering enterprise atmosphere requires us to accelerate the tempo of their digital transformation. They deserve to proceed to live attentive to an ever-changing market," observed Cezar P. Consing, BPI President and CEO. "Our prolonged contract with IBM will support us revolve into extra agile in introducing innovations and reworking how they execute issues in BPI."
"Banks dote BPI are going via a huge shift and embracing an entire transformation of their company with superior capabilities to respond to the wants of ultra-modern linked client," pointed out Martin Jetter, Senior vice president, IBM global expertise services. "we are excited about helping one of the main banks in South East Asia vigour its core IT infrastructure and bring fresh digital experiences with IBM Cloud in a at ease and open ambiance to assist allocate together BPI for the way forward for banking."
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Probably not a headline you ever thought you’d read on diginomica, but the times they are a changin’. The notion is probably not as tall a pot-pipe dream as it sounds.
Canada is in the process of legalizing marijuana by July 2018, fulfilling one of the key drive promises of Prime Minister Justin Trudeau. Ottawa is leaving it up to provincial governments to regulate its legal sale and distribution.
When the government of British Columbia asked for feedback on the best way to manage its legal marijuana market, IBM quickly proffered a regulatory filing, arguing that the province should utilize the distributed ledger technology called blockchain to manage its legal marijuana market.
In the filing, the Armonk, NY-based software giant said:
IBM suggests Blockchain is an pattern mechanism in which BC can transparently capture the history of cannabis through the entire supply chain, ultimately ensuring consumer safety while exerting regulatory control – from seed to sale.
Blockchain is rapidly becoming a world leading technology enabling the assured exchange of value in both digital and tangible assets, while protecting privacy and eliminating fraud. Blockchain offers a shared ledger that is updated and validated in true time with each network participant. It enables equal visibility of activities and reveals where an asset is at any point in time, who owns it and what condition it’s in.
As every schoolboy now knows, blockchains were originally conceived as a public, shared ledger that keeps track of payments in the Bitcoin network.
Bitcoin’s volatility has unnerved off a lot of potential mainstream adoption of blockchain as a payment network.
But companies quickly realized that the core concept of the blockchain could live repurposed for many other chains of custody applications, ranging from diamonds and tomatoes to global supply chains. Or, as in this case, the growth, distribution, sale, regulation, and taxing of your next favorite pastime substance.
Basically, a blockchain is nothing more than a distributed database that’s maintained by a peer-to-peer network of companies involved in almost any shared marketplace. Every company manages and maintains its own copy of the shared ledger.
In the case of British Columbia’s pot initiative, “each party in the industry network” (growers, processors, distributors, retail locations, and regulators) would derive its own ledger copy showing any transactions. The shared ledger would live accessible for anyone with the perquisite credentials to see, allowing regulators to conduct spot audits of every marijuana transaction in the province.
Retailers could identify which farm a particular batch of marijuana came from and what safety inspections were conducted along the way. Reads the IBM pitch:
The Blockchain shared ledger is updated and validated in true time with each network participant. This enables equal visibility of activities and reveals where an asset/product is at any point in time, who owns it and what condition or status it is in. This nature of transparency would bring a fresh flat of visibility and control to the provincial regulators and provide assurance to the multitude of cautious stakeholders regarding the way the management of a cannabis supply chain is rolled out within British Columbia.
IBM’s brief proposal to the government of British Columbia identifies key benefits for each of the parties in a feasible blockchain agreement:
BC Government: Blockchain can support the Provincial Government tangle control of sourcing, selling and pricing of products, therefore can reduce or liquidate black market sales completely.
Producers: Blockchain can assist producers with real-time inventory management, greater projections of supply and demand, and furthermore elicit trends of consumption through data analytics.
Retailers: Although the Government of BC hasn’t confirmed what end-user distribution model will live used, they anticipate that it is likely that government itself will play a role in that process. An interconnected Blockchain network can assist retailers identify supply/demand gaps ways to mitigate those gaps, providing feedback mechanisms to producers, and utilize data to create predictive insights.
IBM has been promoting blockchain aggressively in recent months. In March, the company released IBM Blockchain, which it says is the first enterprise-ready blockchain service based on the Linux Foundation’s open source Hyperledger project.
IBM’s blockchain services are designed to support developers create, deploy and manage blockchain networks on the IBM Cloud and a number of clients believe already done so, ranging from the startup Everledger, which bills itself as the digital vault of the future, to pecuniary services leaders dote Bank of Tokyo-Mitsubishi UFJ, Postal Savings Bank of China and Northern Trust.
A blockchain approach to supply chain management has a number of useful industry advantages. Transactions can live asynchronous—individual buyers and sellers can deal directly with each other.
The Hyperledger stack has cryptographic features that can circumscribe one vendor from spying on another vendor’s transactions. Hyperledger can furthermore live combined with Internet-of-things technology, attaching a tracking device to products in transit and automatically generating data about their progress as they glide through the supply chain.
Using its Watson IoT Platform, IBM makes it feasible to utilize information from devices such as RFID-based locations, barcode-scan events, or device-reported data with IBM’s Blockchain. Devices will live able to communicate to blockchain-based ledgers to update or validate smart contracts.
Built to scale to thousands of users quickly, IBM says its cloud will allow production blockchain networks to live deployed in minutes, running signed, certified and tested Docker images with dashboards and analytics as well as support.
A blockchain is only as kindly as the security of its infrastructure and IBM has spent millions of dollars on teams of security experts, cryptographers, hardware experts, and researchers to design inescapable its blockchain and cloud services are tamper-resistant and free of back door vulnerabilities that allow unauthorized access.
With so much buzz about blockchain and so many projects underway at the moment, we’re about to find out whether the technology is the true thing or, in the case of BC, one toke over the line. I’m becoming a believer.
This just leaves me with two tall questions – what would Bob Marley believe said? Who gets to test the supply chain in the true world? to a inescapable extent I can’t remark the tall Blue consultants out in the field, so to speak. 🙂
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Kubernetes is faultfinding to forward-looking enterprises that continue to propel their IT infrastructures toward maximum functionality, scalability, and flexibility.
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DXWorldEXPO® has organized these issues along 10 tracks, 22 keynotes and generic sessions, and a faculty of 222 of the world's top speakers.
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Technology - The Revolution ContinuesEconomy - The 21st Century EmergesSociety - The tall Issues
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DXWorldEXPO LLC is a Lighthouse Point, Florida-based trade array company and the creator of DXWorldEXPO - Digital Transformation Conference & Expo. The company produces and presents the world's most influential technology events including CloudEXPO, DevOpsSUMMIT, and FinTechEXPO.
Sunday Oct 22ndBuilding the Products Your Customers Need — Alluvium
Company focus:- 1. They support them understand stability- 2. faultless production
Software is there to help, support, and enable experts in the any parts of the system.
Under the hood, fundamentally about dimensionality reduction…folks at the edge pan lofty dimensionality problems day after day. Started with online learning off streaming data for the edge….but faced cultural and organizational friction.
Risk of adoption stalled conversations and POCs. Turned to, hey, what happens if they evaluate your historical data and immediately provide value/build longterm trust.
Historical event analysis — grounding it in true problem (stability score + true sensor data). Using shallow NN (lstm), unsupervised.
Saturday Oct 21st
Listening primarily to Andrew Ng.- Today, the money is in supervised learning, structured data, and ads. He’s spoken before about the revenue Baidu was seeing (even a yoke years ago) using profound learning for ad targeting, and while it’s not the sexiest utilize case, it’s certainly a prudent one.- Ng said he thinks of structure a industry around AI as a multi-year “chess game,” where the goal is to strategically assemble the best data, build models, build the product, and then learn even more from user data — ultimately creating a virtuous cycle.- heartfelt AI company has these components. — ‘strategic data acquisition’ — ‘centralized data warehouse’ — ‘pervasive automation’ — ‘new job descriptions'
Merlon with Bradford. Similar to skymind with pecuniary industry fraud — crimes analysis. - wanted to live here because of how unsexy, true biz problem it is- catalysts: execs loosing jobs, amount of spending, amount of fines- humans in the loop and ML product to support signal if someone opening a bank account should live doing that, notifying staff of users in the word for — potentially atrocious developments- companies spending $10B+ each year on this problem- risk ranking vs interest ranking (comps to his own startup data crawling, learn to rank implementations)- provide a UI, replete stack product- generate some of their own data- what is defensible. Product that ties within prop data, prop ui. Learning cross competitors. - People/HR side is defensible too
Bradford speaking with Matt Turck. - Largely the selfsame pitch around upright AI and why MLaaS/horizontal has — limited potential- Focus on solving higher flat industry problem as opposed to low flat stint that will become commoditized pretty quickly- Revenue - “There is requisite to support people understand how machine learning can unravel their problems and if you don’t Come to them with a solution…but you kindhearted of Come with a really noteworthy piece of technology that could unravel their solution…your ultimately drop the problem back in their lap.”https://www.youtube.com/watch?v=mYu2MnPnCCY
New (to me) commentary on pattern team structures:- Mostly remark polar (very technical team…7 top kids from stanford) or (some guy from trucking business, domain expertise but no clue around tech)- You remark very runt of very smart ML founder and smart industry founder that realize they requisite to labor together and furthermore derive PM, designer, matter matter expert, etc- Sometimes you can overweight the domain expertise in first 1–2…startup commercial sense and savvy is more essential in the beginning
Friday Oct 20th Command C + Command V for the future tech IPOs. S-1
Thursday Oct 19th
Pod: https://overcast.fm/+HzyVPL4iU — AlluviumNotes:
Manufacturing, broadly speaking, falls into two buckets. - Discrete — monorail (car + plane assembly)- Process/Additive — focus for Alluvium- Producing a purport or material (refining oil, chemical processing), this has highly interdependent systems.- reckon of a visual aid…thousands of pipes and boilers wrangled together
Who the user is falls into two groups:
- someone working in oil refinery…typically relying on traditional threshold based results (alarm going off)- team of individuals looking back at historical (corporate or incident resolution)
Product goal is to consolidate that and provide collective intelligence — system flat analysis in true time…humans in the loop looking at feedback. + — labels
Not necessarily considered anomaly detection (thresholds believe already been hit), rather — series of smaller changes and heads up
Technical under the hood:- time string data of different frequency- asset emitting status or sensor sharing temperature- used unsupervised or semi machine learning (user)- reality is most folks aren’t in a position to believe that data..need to start from scratch and start injecting human expertise into the loop- traditional ML (forecasting) and shallow NN (fluffy admit here, prodigy if most folks worry about being perceived as not having profound tech)- built profound empathy and humility for operators (most team lacked industrial DNA and domain knowledge)
General fears of a solution of this type
Corporate execs: fright that these tools that don’t actually know the problem and fright of how execute they retain domain erudition amongst workers that are aging / retire- looking ways at software can build institutional knowledge
Front line side: machines are coming to supersede job- stress and messaging that system has to labor alongside the human
Wednesday Oct 18th
and fragment 2. Very well received by /r/machinelearing
- Neuron — thing that holds a number (between 0–1)…function - Learning: computer finding perquisite weights and biases - Relu replaced sigmoid
- Cost office lofty when not training well, low when performant- local minimum: doable- global minimum: very hard
Tuesday Oct 17thConversation with software engineer spending terminal year seriously exploring the field.Draw to DL:- Potential repercussion over the longterm- Can play a role in bringing software eng practices to ml workflows- Find ML problems to live more moving than APIs/backend plumbing- industry reality that folks are using ML perquisite now
Shallow and fresh field~1 yr of in-depth study can bring you to the edge of the field.
Monday Oct 16th
PodcastNotes:- They believe a lot of resources, this is a tall sphere that we’re well poised to play in.- A lot of corporate speak oddly concluded with a candid and genuine remark that they don’t know of how some (non-silicon) industry models will develop
Found this helpful in trying to understand automatic speech recognition vs natural language processing.
ASR — attempt to determine what, exactly, was said- furthermore known as speech to textNLP —tries to determine the intent of what was said
Saturday October 14th
Post from Erik Bernhardsson who I’ve Come to really respect from afar for discussions about ML, data, and org building.
This piece does a nice job of setting the scene. MOAR DATA =/ perquisite or useful#anotherone
Post.A tweet from a month prior
again…real candid and grounded thoughts from someone who has actually built these systems. valuable to derive this perspective.
Anecdotally institute his reasoning as to why he took the CTO position to live one of the better I’m sharing a evolution in my professional life kindhearted of updates.
Friday October 13th
Pod.Notes — ProductAI
Applied computer vision - Special focus in object detection- Matt is former longtime MSFT researcher. SEA → Beijing office- 2012 mainstream debut / breakthrough for DL on-stage…NYT front page press- ‘I still reckon it’s the early days of DL’ — Supervised is a bit more mature, semi/weakly semi supervision is needed
Applications- Textiles character control (much higher interval testing vs random human sampling) material classification, fiber classification, and color analysis — acquired data via human labels (microscope hooked up to inspection)- object detection within x-rays for safety within transportation- Fashion — style/cust/other attributes- Ecommerce — dealing with billions of images/products on marketplaces and auction sites — semi supervised learning is needed to scale with this volume…models trained via this. - Semi-supervised learning is their key technical innovation - “semi-supervised learning = subset of your data which is labeled with liable character and then using that to inform the repose of the data set”- “weakly, in their definition….when you essentially believe labels for any of your data but it’s unreliable (unbalanced, noisy) so that’s where actually find the biggest value…because the internet is replete of it.”- Scalability…moving away from dependence on human labeling
- China landscape is a bit different in perception. Far more open that folks think. Everyone is sharing notes. Rising tide overall. Information reaches the east but research (published in mandarin) in china doesn’t stream back through to US. Andrew Ng highlighted this on selfsame pod.
Thursday October 12thBox movements in ML space the past year.2013…
Box has now done technical integrations / biz partnerships with - IBM (text, audio)- MSFT (video+audio)- Goog (mostly image)
tl;dr~ Machine learning as a service technology is effective and robust enough~ Customers can remark value in better searching, discovering, organizing enriched media. Far more efficient than humans (or no interaction at all)~ Box can leverage best in class APIs…simply build out the plumbing on top of content management platform. ~ Said APIs are commoditized and become plug+play modular. Box here is an aggregator (HT Ben T) with near user relationship (billing, legal, UX, sales force).
Good mp;A with Aaron this summer.
PostThe more content you believe and the more you utilize the system, the harder it gets to use. It’s counter-intuitive that this should preserve progressing this way. The way to unravel that problem, that scale, is through machine learning
Wednesday October 11thSkymind — deep learning for the enterprise…an org and CTO I’ve been very intrigued by.
Notes from variety of sources.- Podcast on Archillect.- Tech talk, talk 2- HN and /r/ML
Business:- Largely serving China (healthcare) and Japan (banking, tech)-US is more stable but conservative line of business- 20 distributed people. - Long sales in suits…working with systems integrators to scale selling to these large organizations. - Tech agnostic…go to where data and dollars are. JVM systems.- Open-core
Applications and solutions:- Fraud detection (credit cards, online banking)- Detecting illegal mining activity via satellite- Network intrusion (packet analysis)- Predicting machine failure- Stock market trend prediction (up/down)- Simbox fraud- Finding similar parts for large vehicles
DL can live kindly at time string data, kindly at finding patterns in behaviorProblems largely revolving around analytics, churn prediction, recommendation engines
Technology:- Created DL4J - Main thing is production deployment software and a service flat agreement for models they build.- Not necessarily always DL, whatever the problem calls for- believe built a lot of the software stack
Tuesday October 10thhttps://www.youtube.com/watch?v=IMoQPvXMkJw
Building robust machine learning systems- Ravelin — Protect merchants from credit card fraud- 1–3% of revenue…cost for the merchant- staid precision/recall tradeoff. FP as a consumer (just checking, they reckon there may live fraud…False Neg — you don’t spot the fraud)- I reckon most engineers would live horrified at the status of machine learning tooling (training, testing, deploying)- ML systems — High complexity — Current decisions repercussion future performance — Loss office doesn’t represent heartfelt goal
- Training models — How execute they design positive the world they present to the model is the view of the world they actually want to learn?
- Labels & truth — Delayed labels….takes many weeks/months to acocunt for so if you just train on terminal 2–3mos…you actually don’t believe that captured view of labels.
- Testing — pull of set of examples you should *always* derive right — use as unit test — generate synthetic data for worst case scenarios (loading up credit cards in 10min, spending 100k)
Deploying- Model provenance — who trained the model…when…what git hash….on what data…which hyperparameters…..train/test performance — Shipping model…share as much code as feasible between offline and online — Release gradually. A/B test. — Score in live with both models. Inspect biggest disagreements.
Automation:- Unfortunately near up training on *data_1_final_edited_really_final(1)(2).csv*
Monday October 9th
#AI methods ranked by econ value Supervised Learning Transfer Learning Unsupervised Learning Reinforcement Learning
Older notes to live backfilled
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