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000-M602 | IBM Banking Industry Solutions Sales(R) Mastery Test v1

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000-M602 - IBM Banking Industry Solutions Sales(R) Mastery Test v1 - braindump

Vendor IBM
Exam Number 000-M602
Exam Name IBM Banking Industry Solutions Sales(R) Mastery Test v1
Questions 30 Q & A
Recent Update February 12, 2019
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000-M602 exam Dumps Source : IBM Banking Industry Solutions Sales(R) Mastery Test v1

Test Code : 000-M602
Test Name : IBM Banking Industry Solutions Sales(R) Mastery Test v1
Vendor Name : IBM
Q&A : 30 Real Questions

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IBM IBM Banking Industry Solutions

IBM (IBM) Getting neutral Press coverage, InfoTrie reviews | killexams.com Real Questions and Pass4sure dumps

Media insurance about IBM (NYSE:IBM) has been trending neutral this week, in accordance with InfoTrie Sentiment. The research group ranks the sentiment of news insurance via examining more than six thousand news 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 also gave press coverage in regards to the expertise enterprise an information buzz score of 1 out of 10, indicating that contemporary news coverage is extremely unlikely to have an impact on the company’s share rate within the near future.

listed below are one of the vital media stories that may also have 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 high 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 business 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 return on fairness of sixty eight.sixty one%. The business had profits of $21.76 billion for the quarter, in comparison to the consensus estimate of $21.seventy nine billion. all 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 share for the latest fiscal year.

The enterprise also recently introduced a quarterly dividend, which will be paid on Saturday, March ninth. investors of listing on Friday, February eighth could be 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 yield of four.seventy two%. IBM’s dividend payout ratio is right now 45.47%.

IBM declared that its Board of directors has initiated a share buyback application on Tuesday, October 30th that makes it possible for the business 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 couple of equities research analysts have 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 financial institution of Canada restated an “outperform” score and issued a $one hundred sixty.00 price 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 have rated the inventory with a promote score, ten have assigned a dangle ranking and eight have 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 also be 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 story on another website, it become illegally stolen and reposted in violation of united states and foreign copyright legislations. The proper edition of this story can be 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 business international. Its Cognitive options phase 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 social software, skill administration solutions, and tailor-made business solutions; and transaction processing application that runs mission-vital systems in banking, airlines, and retail industries.

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Made in IBM Labs: IBM to Open Banking core of Excellence in Vietnam | killexams.com Real Questions and Pass4sure dumps

source: IBM

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 new functions based on open technologies. Banks might be capable of use supplies on the core to satisfy growing purchaser demand for brand spanking new banking products.

The banking center will be found in the IBM Vietnam headquarters in Hanoi and staffed with IBM banking and technical experts. The center 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 high efficiency computing solutions that guarantee fine and safety during the finished lifestyles cycle of banking operations.

moreover, in 2008 IBM will be making enormous investments to enhance a framework in response to services oriented structure (SOA) to help banks modernize their core banking techniques.

A bank's core device provides the groundwork for its business operations. for instance, it tactics transactions to update client mark downs and credit card bills and provides bank-to-bank features akin to wire switch and treasury administration. IBM's new 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 new center 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 be capable of use IBM open infrastructure applied sciences together with net capabilities and virtualization; and software equipment similar to DB2, WebSphere and Tivoli. together, these technologies will help a provider oriented structure to make disparate records attainable throughout multiple traces of enterprise inside an business. as a result, banks can be 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 have IBM convey their event with banks everywhere to Vietnam," mentioned Mr. Dao Minh Tuan, CIO of Vietcombank. "we're keen to use such skills and technologies to offer world-class monetary items for our purchasers."

"Vietnam's transforming into economic system represents an outstanding chance for IBM and the country's banking trade, because it looks to convey new 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 be a globally-built-in useful resource for banks in Vietnam to construct next-generation core banking techniques," brought Ian Hurst, IBM's usual supervisor of Banking and monetary Markets Industries.

In concert with the turning out to be financial system, a myriad of latest banking functions are getting regularly occurring in Vietnam. in line with the State financial institution of Vietnam, in 2006 the number of ATMs grew 21%, whereas the number of financial 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 opportunity for increase.

"We welcome IBM's Banking middle of Excellence," talked about Mr. Ta Quang Tien, Director of suggestions know-how department on the State bank of Vietnam. "The core will support banks in Vietnam reside aggressive with the latest applied sciences, in an effort to serve the need generated by way of anticipated growth."

the new Banking middle of Excellence in Vietnam will be part 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 excessive performance On Demand solutions (HiPODS) Lab; the IBM Poughkeepsie Lab in ny; and the IBM software options Lab in Seoul, Korea. These facilities help 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 center of Excellence, please consult with http://www-01.ibm.com/methods/z/feature022607/

For extra tips about IBM's excessive efficiency Computing On Demand solutions Lab, please consult with http://www-128.ibm.com/developerworks/websphere/zones/hipods/

For greater assistance about IBM device z, please talk over with http://www-03.ibm.com/programs/z/


u.s. : IBM features signals a USD$260M agreement with financial institution of the Philippine Islands to assist the financial institution's Digital Transformation | killexams.com Real Questions and Pass4sure dumps

Feb 01, 2019 (Euclid Infotech Ltd via COMTEX) -- IBM and the bank of the Philippine Islands (BPI), a universal bank headquartered in Manila, have 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 building capabilities to speed 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 keep 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 part of the Digital adventure answer, IBM and BPI have also designed an IT environment that helps an open API financial 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 continual enhancements for financial institution branches with new know-how as well as other monetary capabilities apps for valued clientele.

The features offered via IBM will have "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 method designed by IBM for BPI.

"The unexpectedly altering enterprise atmosphere requires us to accelerate the tempo of our digital transformation. We deserve to proceed to be attentive to an ever-changing market," observed Cezar P. Consing, BPI President and CEO. "Our prolonged contract with IBM will support us turn into extra agile in introducing innovations and reworking how we do issues in BPI."

"Banks like 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 new digital experiences with IBM Cloud in a at ease and open ambiance to assist put together BPI for the way forward for banking."


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Proposal – IBM blockchain to manage marijuana supply chain in British Columbia | killexams.com real questions and Pass4sure dumps

Bob Marley marijuanaProbably not a headline you ever thought you’d read on diginomica, but the times they are a changin’.  The idea is probably not as big a pot-pipe dream as it sounds.

Canada is in the process of legalizing marijuana by July 2018, fulfilling one of the key campaign 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 use 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 ideal 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 real 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 scared 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 be 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 business network” (growers, processors, distributors, retail locations, and regulators) would get its own ledger copy showing all transactions. The shared ledger would be accessible for anyone with the right 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 real 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 state it is in. This type of transparency would bring a new level 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 possible blockchain agreement:

BC Government: Blockchain can help the Provincial Government take control of sourcing, selling and pricing of products, therefore can reduce or eliminate black market sales completely.

Producers: Blockchain can assist producers with real-time inventory management, greater projections of supply and demand, and also elicit trends of consumption through data analytics.

Retailers: Although the Government of BC hasn’t confirmed what end-user distribution model will be used, we 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 use 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 help developers create, deploy and manage blockchain networks on the IBM Cloud and a number of clients have already done so, ranging from the startup Everledger, which bills itself as the digital vault of the future, to financial services leaders like 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 business advantages. Transactions can be asynchronous—individual buyers and sellers can deal directly with each other.

The Hyperledger stack has cryptographic features that can limit one vendor from spying on another vendor’s transactions.  Hyperledger can also be combined with Internet-of-things technology, attaching a tracking device to products in transit and automatically generating data about their progress as they move through the supply chain.

Using its Watson IoT Platform, IBM makes it possible to use information from devices such as RFID-based locations, barcode-scan events, or device-reported data with IBM’s Blockchain. Devices will be 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 be deployed in minutes, running signed, certified and tested Docker images with dashboards and analytics as well as support.

A blockchain is only as good as the security of its infrastructure and IBM has spent millions of dollars on teams of security experts, cryptographers, hardware experts, and researchers to make certain 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 real thing or, in the case of BC, one toke over the line. I’m becoming a believer.

This just leaves me with two BIG questions – what would Bob Marley have said? Who gets to test the supply chain in the real world? Somehow I can’t see the Big Blue consultants out in the field, so to speak. 🙂

Image credit - public images


Kubernetes Powered Central Go Modules | @KubeSUMMIT @JFrog @LeonStigter #Serverless #Containers #Docker #Kubernetes | killexams.com real questions and Pass4sure dumps

Building a Kubernetes Powered Central Go Modules Repository

Register for this session Here

Today, Kubernetes is the defacto standard if you want to run container workloads in a production environment. As we set out to build our next generation of products, and run them smoothly in the cloud, we needed to move to Kubernetes too! In the process of building tools like KubeXray and GoCenter we learned a whole bunch.

Join this talk to learn how to get started with Kubernetes and how we got started at JFrog building our new tools. After the session you will know:How we got to Kubernetes (and why we chose it)

How to know what you have in your apps and containers (and how we built ours)

How we're running GoCenter at scale.

Speaker Bio:

Leon is a Developer Advocate at JFrog where his focus is on helping developers get the most out of their DevOps tools with the end goal of enabling those developers to achieve the Liquid Software vision. As an outspoken supporter of developers Leon advocates for the needs of developers within JFrog and helps to drive open source tooling and Developer Relations strategy.Leon is passionate about serverless and container technologies and enjoys writing code and speaking and blogging about that. In his personal life, he's on a mission to taste cheesecake in every city he visits (suggestions are welcome @LeonStigter).

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    The Feed | killexams.com real questions and Pass4sure dumps

    Sunday Oct 22ndBuilding the Products Your Customers Need — Alluvium

    Company focus:- 1. We help them understand stability- 2. Perfect production

    Software is there to help, support, and enable experts in the all parts of the system.

    Under the hood, fundamentally about dimensionality reduction…folks at the edge face high 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 we evaluate your historical data and immediately provide value/build longterm trust.

    Historical event analysis — grounding it in real problem (stability score + real 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 couple years ago) using deep learning for ad targeting, and while it’s not the sexiest use case, it’s certainly a prudent one.- Ng said he thinks of building a business around AI as a multi-year “chess game,” where the goal is to strategically gather the best data, build models, build the product, and then learn even more from user data — ultimately creating a virtuous cycle.- True AI company has these components.  — ‘strategic data acquisition’ — ‘centralized data warehouse’ — ‘pervasive automation’ — ‘new job descriptions'

    Merlon with Bradford. Similar to skymind with financial industry fraud — crimes analysis. - wanted to be here because of how unsexy, real biz problem it is- catalysts: execs loosing jobs, amount of spending, amount of fines- humans in the loop and ML product to help signal if someone opening a bank account should be doing that, notifying staff of users in the news for — potentially bad 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, full 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 same pitch around vertical AI and why MLaaS/horizontal has — limited potential- Focus on solving higher level business problem as opposed to low level task that will become commoditized pretty quickly- Revenue - “There is need to help people understand how machine learning can solve their problems and if you don’t come to them with a solution…but you kind of come with a really great piece of technology that could solve their solution…your ultimately drop the problem back in their lap.”https://www.youtube.com/watch?v=mYu2MnPnCCY

    New (to me) commentary on ideal team structures:- Mostly see polar (very technical team…7 top kids from stanford) or (some guy from trucking business, domain expertise but no clue around tech)- You see very little of very smart ML founder and smart business founder that realize they need to work together and also get PM, designer, subject matter expert, etc- Sometimes you can overweight the domain expertise in first 1–2…startup commercial sense and savvy is more important 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 substance or material (refining oil, chemical processing), this has highly interdependent systems.- Think 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 level analysis in real time…humans in the loop looking at feedback. + — labels

    Not necessarily considered anomaly detection (thresholds have already been hit), rather — series of smaller changes and heads up

    Technical under the hood:- time series data of different frequency- asset emitting state or sensor sharing temperature- used unsupervised or semi machine learning (user)- reality is most folks aren’t in a position to have that data..need to start from scratch and start injecting human expertise into the loop- traditional ML (forecasting) and shallow NN (fluffy answer here, wonder if most folks worry about being perceived as not having deep tech)- built deep empathy and humility for operators (most team lacked industrial DNA and domain knowledge)

    General fears of a solution of this type

    Corporate execs: fear that these tools that don’t actually know the problem and fear of how do we retain domain knowledge amongst workers that are aging / retire- looking ways at software can build institutional knowledge

    Front line side: machines are coming to replace job- stress and messaging that system has to work alongside the human

    Wednesday Oct 18th

    and Part 2. Very well received by /r/machinelearing

    - Neuron — thing that holds a number (between 0–1)…function - Learning: computer finding right weights and biases - Relu replaced sigmoid

    - Cost function high when not training well, low when performant- local minimum: doable- global minimum: very hard

  • find downhill direction and steepest descent- a network learning is minimizing a cost function using gradient descent
  • Tuesday Oct 17thConversation with software engineer spending last year seriously exploring the field.Draw to DL:- Potential impact over the longterm- Can play a role in bringing software eng practices to ml workflows- Find ML problems to be more interesting than APIs/backend plumbing- Business reality that folks are using ML right now

    Shallow and new field~1 yr of in-depth study can bring you to the edge of the field.

    Commentary on the state of tooling being completely lacking…state of javascript 10 years ago - model training, deployment, tracking, reproducibility

    Monday Oct 16th

    PodcastNotes:- We have a lot of resources, this is a big field that we’re well poised to play in.- A lot of corporate speak oddly concluded with a candid and genuine comment that they don’t know of how some (non-silicon) business 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- also 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 =/ right 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 get this perspective.

    Anecdotally found his reasoning as to why he took the CTO position to be one of the better I’m sharing a development in my professional life kind 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 think it’s the early days of DL’ — Supervised is a bit more mature, semi/weakly semi supervision is needed

    Applications- Textiles Quality 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 our key technical innovation - “semi-supervised learning = subset of your data which is labeled with reliable quality and then using that to inform the rest of the data set”- “weakly, in our definition….when you essentially have labels for all of your data but it’s unreliable (unbalanced, noisy) so that’s where actually find the biggest value…because the internet is full 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 flow back through to US. Andrew Ng highlighted this on same 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 see 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 end user relationship (billing, legal, UX, sales force).

    Good Q&A with Aaron this summer.

    PostThe more content you have and the more you use the system, the harder it gets to use. It’s counter-intuitive that this should keep progressing this way. The way to solve 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 be good at time series data, good 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 level agreement for models we build.- Not necessarily always DL, whatever the problem calls for- Have 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- Serious precision/recall tradeoff. FP as a consumer (just checking, we think there may be fraud…False Neg — you don’t spot the fraud)- I think most engineers would be horrified at the state of machine learning tooling (training, testing, deploying)- ML systems — High complexity — Current decisions impact future performance — Loss function doesn’t represent true goal

    - Training models — How do we make sure the world we present to the model is the view of the world we actually want to learn?

    - Labels & truth — Delayed labels….takes many weeks/months to acocunt for so if you just train on last 2–3mos…you actually don’t have that captured view of labels.

    - Testing — pull of set of examples you should *always* get 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 possible between offline and online — Release gradually. A/B test.  — Score in live with both models. Inspect biggest disagreements.

    Automation:- Unfortunately end 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 be backfilled



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